Description:FIELD OF DISCLOSURE
[0001] The present disclosure relates generally relates to the field of wildlife monitoring and conservation technologies. More specifically, it pertains to an animal species detection and classification system for forest wild life monitoring using deep learning.
BACKGROUND OF THE DISCLOSURE
[0002] Wildlife monitoring has long been a critical area of research for biologists, ecologists, conservationists, and forest management authorities. The systematic study of animal species within forest ecosystems serves as a cornerstone for understanding biodiversity, ecological balance, and conservation requirements. Over the years, various methods have been employed to monitor wildlife, ranging from manual observation to the integration of sophisticated technological systems. The historical approaches, however, have been fraught with limitations such as labor intensiveness, limited coverage, and lack of precision. With the advent of modern computational techniques and artificial intelligence, new avenues have emerged for detecting and classifying animal species with greater efficiency, accuracy, and scalability.
[0003] The early efforts in wildlife monitoring were primarily manual and relied heavily on direct human observation. Researchers and forest rangers would spend countless hours in natural habitats, documenting animal sightings, recording vocalizations, and tracking footprints or scat for species identification. While these traditional approaches provided valuable data, they were constrained by human fatigue, subjectivity in observation, and the difficulty of covering large and dense forested areas. In addition, many species are nocturnal, elusive, or camouflaged, making it nearly impossible for human observers to detect them consistently. These challenges highlighted the need for automated systems that could augment or replace manual observation while providing a more reliable and continuous stream of data.
[0004] Camera traps marked a significant milestone in the history of wildlife monitoring. The deployment of motion-triggered cameras across forest areas allowed researchers to capture images and videos of animals in their natural habitats without human presence. Camera traps provided an unobtrusive means of collecting data, thereby reducing the disturbance to wildlife and enabling the recording of rare and elusive species. However, the use of camera traps introduced new challenges. The vast number of images collected created an overwhelming demand for manual labeling and annotation. Each image had to be examined by experts to determine the presence and species of animals, which required considerable time and expertise. Furthermore, false triggers caused by vegetation movement, changes in lighting, or environmental conditions led to a large volume of irrelevant data that further burdened the manual analysis process.
[0005] Acoustic monitoring emerged as another important technique in wildlife research. Many animal species communicate using distinctive vocalizations, and these acoustic signatures can serve as reliable identifiers. Autonomous recording units (ARUs) have been deployed in forests to continuously record ambient sounds, providing a rich dataset of animal calls. Acoustic monitoring has been particularly effective for detecting bird species, amphibians, and certain mammals. However, this method faces limitations such as background noise interference, overlapping calls from multiple species, and the difficulty of distinguishing between similar acoustic patterns. Traditional signal processing methods often struggle to capture the complexities of animal vocalizations, necessitating more advanced techniques for accurate classification.
[0006] The field of remote sensing has also contributed to wildlife monitoring by providing satellite imagery and aerial photography that can be used to assess habitat conditions and track large animal populations. Remote sensing data, however, is often limited in its ability to detect smaller species or provide fine-grained details about individual animals. While useful for habitat analysis and population estimation, remote sensing requires integration with other methods for comprehensive species detection and classification.
[0007] As computational capabilities advanced, researchers began experimenting with machine learning techniques for automated wildlife monitoring. Classical machine learning methods such as support vector machines (SVMs), k-nearest neighbors (k-NN), and random forests were employed for species classification tasks. These algorithms required the extraction of handcrafted features from images, sounds, or sensor data. For example, in image-based classification, features such as shape, texture, and color histograms were manually engineered and then fed into classifiers. Similarly, in acoustic monitoring, spectrogram features or mel-frequency cepstral coefficients (MFCCs) were commonly used. While these approaches provided promising results, they were heavily dependent on the quality of the handcrafted features. Feature engineering was a laborious process that required domain expertise, and the resulting models often lacked generalization when applied to new datasets or environments.
[0008] The limitations of traditional machine learning approaches paved the way for deep learning, which has revolutionized the field of pattern recognition and classification. Deep learning, particularly convolutional neural networks (CNNs), eliminated the need for manual feature engineering by automatically learning hierarchical representations directly from raw data. In image-based wildlife monitoring, CNNs have demonstrated remarkable performance in detecting and classifying animal species. These models can capture complex visual patterns such as fur textures, body shapes, and distinctive markings that are difficult to quantify manually. Similarly, recurrent neural networks (RNNs) and their variants, such as long short-term memory (LSTM) networks, have been used to model temporal dependencies in acoustic signals, enabling more accurate identification of species from vocalizations.
[0009] The integration of deep learning into wildlife monitoring has been further facilitated by the availability of large-scale datasets and increased computational power. Camera trap initiatives, such as Snapshot Serengeti, have generated millions of labeled images, providing a valuable resource for training deep learning models. Advances in graphics processing units (GPUs) and cloud computing have made it feasible to train complex models on large datasets within a reasonable timeframe. Furthermore, the development of transfer learning techniques has enabled researchers to leverage pre-trained models on general datasets, such as ImageNet, and fine-tune them for wildlife-specific tasks. This approach has significantly reduced the data requirements and improved the performance of deep learning models in ecological applications.
[0010] Despite these advancements, several challenges persist in the deployment of deep learning systems for animal species detection and classification in forest environments. One major challenge is the variability of environmental conditions. Forests are dynamic ecosystems where lighting, vegetation density, and weather conditions can drastically affect the quality of images and audio recordings. Deep learning models often struggle with such variations, leading to reduced accuracy in real-world deployments. Another challenge is the issue of class imbalance. In many datasets, certain species are overrepresented while others are extremely rare. Deep learning models trained on imbalanced datasets tend to favor the majority classes, resulting in poor detection of rare or endangered species that are often the most critical for conservation efforts.
[0011] Another critical issue is the computational and energy requirements of deep learning models. Deploying such models in remote forest areas often necessitates edge computing devices that operate on limited power sources, such as batteries or solar panels. The resource-intensive nature of deep learning poses challenges for real-time processing and continuous monitoring in such environments. Additionally, the transfer of large volumes of data from remote forest sites to centralized servers for processing may be constrained by limited network connectivity. This necessitates the development of lightweight models and on-device inference capabilities to enable effective field deployment.
[0012] The ethical and ecological implications of wildlife monitoring also warrant careful consideration. While automated systems provide valuable insights, they must be designed to minimize disturbance to wildlife and respect ecological integrity. Camera traps and acoustic sensors, if not carefully deployed, may interfere with animal behavior or introduce artificial elements into natural habitats. Moreover, the collection and storage of wildlife data raise concerns about data privacy, especially in contexts where human activities may also be recorded. Ensuring ethical data management practices is crucial for maintaining public trust and ecological sustainability.
[0013] In addition to ecological monitoring, automated animal species detection has significant applications in areas such as anti-poaching efforts, forest management, and human-wildlife conflict mitigation. Real-time detection systems can alert authorities to the presence of endangered species or potential threats, enabling timely intervention. Furthermore, the integration of deep learning with Internet of Things (IoT) technologies and wireless sensor networks opens possibilities for large-scale, distributed monitoring systems that provide comprehensive coverage of forest ecosystems. These developments underscore the transformative potential of artificial intelligence in addressing pressing environmental challenges.
[0014] Beyond species detection, deep learning also holds promise for behavioral analysis and ecological modeling. By analyzing sequences of images or audio recordings, models can infer patterns of animal behavior, migration routes, and social interactions. Such insights are invaluable for understanding ecosystem dynamics and predicting the impacts of environmental changes, such as deforestation or climate change, on wildlife populations. The fusion of multimodal data including images, audio, and environmental sensor readings can provide a holistic view of forest ecosystems and enhance the robustness of species classification systems.
[0015] The field continues to evolve rapidly, with ongoing research focusing on improving model accuracy, scalability, and interpretability. Techniques such as attention mechanisms, graph neural networks, and self-supervised learning are being explored to enhance the performance of deep learning models in wildlife applications. Attention mechanisms, for instance, allow models to focus on the most relevant regions of an image or segments of audio, thereby improving classification accuracy. Graph neural networks enable the modeling of relationships between species, habitats, and environmental factors, facilitating more context-aware predictions. Self-supervised learning approaches aim to leverage unlabeled data, which is abundant in wildlife monitoring, to pre-train models and reduce the reliance on labor-intensive annotation.
[0016] Thus, in light of the above-stated discussion, there exists a need for an animal species detection and classification system for forest wild life monitoring using deep learning.
SUMMARY OF THE DISCLOSURE
[0017] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0018] According to illustrative embodiments, the present disclosure focuses on an animal species detection and classification system for forest wild life monitoring using deep learning which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0019] An objective of the present disclosure is to build an adaptive classification model capable of distinguishing visually and acoustically similar species by learning subtle inter-species variations.
[0020] Another objective of the present disclosure is to develop a deep learning-based system capable of detecting and classifying multiple animal species from forest environments with high accuracy and robustness.
[0021] Another objective of the present disclosure is to integrate multi-modal data sources such as images, videos, and audio signals from camera traps, drones, and acoustic sensors for comprehensive wildlife monitoring.
[0022] Another objective of the present disclosure is to overcome limitations of conventional observation methods by enabling automated, non-intrusive, and scalable monitoring of forest wildlife.
[0023] Another objective of the present disclosure is to design deep neural network architectures that can effectively handle imbalanced datasets by applying augmentation, transfer learning, or weighted loss functions.
[0024] Another objective of the present disclosure is to enhance animal detection in low-visibility conditions, such as dense forests or poor lighting, using advanced feature extraction and noise reduction techniques.
[0025] Another objective of the present disclosure is to provide real-time species identification and population monitoring that assists conservationists and government agencies in ecological decision-making.
[0026] Another objective of the present disclosure is to minimize false positives and false negatives in animal detection by employing hybrid approaches combining computer vision and audio signal analysis.
[0027] Another objective of the present disclosure is to create a scalable system that can be deployed across different forest ecosystems with minimal manual recalibration.
[0028] Yet another objective of the present disclosure is to contribute toward biodiversity preservation by generating reliable wildlife population insights, migration patterns, and habitat usage for conservation strategies.
[0029] In light of the above, an animal species detection and classification system for forest wild life monitoring using deep learning comprises a data acquisition module configured to receive and record raw audio signals from forest environments using one or more audio recording devices. The system also includes a pre-processing module configured to segment the raw audio signals into audio slices in the time domain and convert the audio slices into spectrogram representations capturing frequency characteristics. The system also includes a hybrid data augmentation module configured to enhance the spectrograms by applying frequency-domain augmentations. The system also includes a deep learning-based classification module comprising one or more neural network architectures trained on the augmented dataset to detect and classify distinct animal species vocalizations. The system also includes a monitoring and output module configured to generate species-specific detection and classification outputs for wildlife monitoring and ecological analysis.
[0030] In one embodiment, the data acquisition module comprises a plurality of strategically positioned audio recording devices configured to capture audio signals from multiple forest locations simultaneously.
[0031] In one embodiment, the pre-processing module is further configured to remove background noise and normalize audio signals before generating spectrogram representations.
[0032] In one embodiment, the pre-processing module generates Mel-spectrograms or Short-Time Fourier Transform (STFT) spectrograms as the frequency-domain representations.
[0033] In one embodiment, the hybrid data augmentation module applies one or more of pitch shifting, time stretching, or frequency masking to the spectrograms.
[0034] In one embodiment, the hybrid data augmentation module dynamically updates augmentation parameters based on the performance metrics of the deep learning-based classification module.
[0035] In one embodiment, the deep learning-based classification module comprises a convolutional neural network (CNN), a recurrent neural network (RNN), or a hybrid CNN-RNN architecture for species detection.
[0036] In one embodiment, the deep learning-based classification module is trained using supervised learning on labeled datasets of animal vocalizations.
[0037] In one embodiment, the monitoring and output module provides real-time alerts or notifications upon detecting specific animal species.
[0038] In one embodiment, the monitoring and output module stores historical detection data for ecological analysis and trend monitoring.
[0039] These and other advantages will be apparent from the present application of the embodiments described herein.
[0040] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0041] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0042] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0043] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0044] FIG. 1 illustrates a flowchart outlining sequential step involved in an animal species detection and classification system for forest wild life monitoring using deep learning, in accordance with an exemplary embodiment of the present disclosure;
[0045] FIG. 2 illustrates a flowchart of ML system, in accordance with an exemplary embodiment of the present disclosure;
[0046] FIG. 3 illustrates a flowchart of hybrid data augmentation technique, in accordance with an exemplary embodiment of the present disclosure.
[0047] Like reference, numerals refer to like parts throughout the description of several views of the drawing;
[0048] The animal species detection and classification system for forest wild life monitoring using deep learning, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0049] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0050] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0051] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0052] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0053] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0054] Referring now to FIG. 1 to FIG. 3 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates an animal species detection and classification system for forest wild life monitoring using deep learning, in accordance with an exemplary embodiment of the present disclosure.
[0055] An animal species detection and classification system for forest wild life monitoring using deep learning 100 comprises a data acquisition module 102 configured to receive and record raw audio signals from forest environments using one or more audio recording devices. The data acquisition module 102 comprises a plurality of strategically positioned audio recording devices configured to capture audio signals from multiple forest locations simultaneously.
[0056] The system also includes a pre-processing module 104 configured to segment the raw audio signals into audio slices in the time domain and convert the audio slices into spectrogram representations capturing frequency characteristics. The pre-processing module 104 is further configured to remove background noise and normalize audio signals before generating spectrogram representations. The pre-processing module 104 generates Mel-spectrograms or Short-Time Fourier Transform (STFT) spectrograms as the frequency-domain representations.
[0057] The system also includes a hybrid data augmentation module 106 configured to enhance the spectrograms by applying frequency-domain augmentations. The hybrid data augmentation module 106 applies one or more of pitch shifting, time stretching, or frequency masking to the spectrograms. The hybrid data augmentation module 106 dynamically updates augmentation parameters based on the performance metrics of the deep learning-based classification module.
[0058] The system also includes a deep learning-based classification module 108 comprising one or more neural network architectures trained on the augmented dataset to detect and classify distinct animal species vocalizations. The deep learning-based classification module 108 comprises a convolutional neural network (CNN), a recurrent neural network (RNN), or a hybrid CNN-RNN architecture for species detection. The deep learning-based classification module 108 is trained using supervised learning on labeled datasets of animal vocalizations.
[0059] The system also includes a monitoring and output module 110 configured to generate species-specific detection and classification outputs for wildlife monitoring and ecological analysis. The monitoring and output module 110 provides real-time alerts or notifications upon detecting specific animal species. The monitoring and output module 110 stores historical detection data for ecological analysis and trend monitoring.
[0060] FIG. 1 illustrates a flowchart outlining sequential step involved in an animal species detection and classification system for forest wild life monitoring using deep learning.
[0061] At 102, the process begins with a data acquisition module, which is responsible for receiving and recording raw audio signals from the forest environment. This module leverages one or more strategically deployed audio recording devices that capture environmental sounds, including calls and vocalizations of various wildlife species. The module ensures continuous and high-fidelity data collection, forming the foundational dataset for subsequent processing.
[0062] At 104, once the raw audio signals are collected, they are transferred to the pre-processing module. This module plays a critical role in preparing the data for analysis by segmenting the continuous audio recordings into manageable audio slices in the time domain. Each audio slice is then transformed into a spectrogram representation, which captures both temporal and frequency characteristics of the sound. The spectrograms serve as the input format for deep learning models, enabling them to identify subtle variations in frequency patterns that distinguish one species from another. This step ensures that the raw, unstructured audio data is converted into a structured and informative representation suitable for machine learning.
[0063] At 106, following pre-processing, the spectrograms are enhanced in the hybrid data augmentation module. This module applies frequency-domain augmentation techniques to increase the diversity of the training dataset, which is crucial for improving the robustness of the deep learning models. By simulating variations such as shifts in pitch, noise injection, and other frequency-domain transformations, the module helps the system generalize better to real-world conditions where recordings may vary due to environmental factors or recording device limitations. This augmentation step ensures that the subsequent classification models are trained on a richer and more representative dataset, reducing the risk of overfitting.
[0064] At 108, the augmented spectrograms are then fed into the deep learning-based classification module, which forms the core analytical engine of the system. This module comprises one or more neural network architectures that have been trained on the enhanced dataset to detect and classify distinct animal species based on their vocalizations. The neural networks analyze frequency and temporal features extracted from the spectrograms, learning patterns that are unique to each species. Through this process, the module is capable of accurately distinguishing among multiple species, even in complex and noisy forest environments. The use of hybrid architectures allows the system to leverage complementary strengths of different neural network types, enhancing detection accuracy and classification performance.
[0065] At 110, the processed results are handled by the monitoring and output module. This module generates species-specific detection and classification outputs, providing actionable insights for wildlife monitoring and ecological analysis. The outputs can include real-time alerts, reports on species presence, vocalization patterns, and statistical summaries that inform conservation strategies and ecological studies. By integrating the entire pipeline from raw audio acquisition to deep learning-based classification and output generation the system provides a comprehensive solution for automated wildlife monitoring, enabling researchers and forest managers to observe, document, and analyze animal species with minimal human intervention and maximal accuracy.
[0066] FIG. 2 illustrates a flowchart of ML system.
[0067] FIG. 2 depicts a comprehensive framework for animal species detection and classification using audio signals, emphasizing both the training and classification workflows. At the foundation, the system relies on datasets, which are divided into training and testing subsets, typically following an 80/20 split. This ensures that the model learns from a majority portion of the data while reserving a smaller portion for evaluating its performance and generalization capability. Raw audio signals, captured from the forest environment through recording devices, feed into the data acquisition module, which acts as the primary interface between the external environment and the system. This module is responsible for collecting high-quality audio signals that include animal vocalizations alongside environmental noises.
[0068] Following data acquisition, the audio signals undergo feature extraction, where critical characteristics of the sounds are transformed into a format suitable for machine learning models. This step may involve converting time-domain audio slices into spectrograms to represent frequency characteristics, allowing the system to better capture patterns inherent to different species’ vocalizations. These extracted features are then fed into the classification stage, which utilizes multiple machine learning and deep learning approaches. The image highlights a hybrid approach: traditional classifiers such as Support Vector Machines (SVM), Naïve Bayes, k-Nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Decision Trees (DTree) are trained on the 80% training dataset, while a convolutional neural network (CNN) model is separately employed to leverage its capability for hierarchical feature learning from complex audio patterns. Both classification pathways contribute to predicting species-specific labels.
[0069] The evaluation module forms the final stage, assessing the performance of the classification models using the reserved 20% testing dataset. This module ensures that the system can accurately distinguish between species while handling environmental variability, overlapping calls, and other real-world complexities present in forest audio recordings.
[0070] FIG. 3 illustrates a flowchart of hybrid data augmentation technique.
[0071] The process begins with the collection of raw audio recordings, which serve as the primary input for the system. These recordings typically contain diverse audio signals that need to be standardized and enriched to improve the robustness of the model. The raw audio undergoes a series of time-domain processing steps, which include chunk extraction, time shifting, background noise mixing, and time interval dropout. Chunk extraction involves segmenting the continuous audio into smaller manageable pieces, making it easier for the model to analyze short-term patterns. Time shifting adjusts the temporal position of the audio signals to increase variability, while background noise mixing introduces additional audio variations to simulate real-world environments. Time interval dropout further enhances the model's ability to generalize by randomly omitting small portions of the audio signals.
[0072] Once the time-domain processing is complete, the audio data is converted into spectrograms. This transformation converts the one-dimensional audio signal into a two-dimensional representation, displaying both frequency and time information. Spectrograms are essential for deep learning models because they enable the extraction of complex patterns that are not easily discernible in raw audio form. Following the spectrogram conversion, data augmentation techniques are applied to further enrich the dataset and enhance model performance. The augmentation process includes pitch shifting, time stretching, and frequency scaling. Pitch shifting alters the perceived pitch of the audio without changing its duration, enabling the model to recognize sounds across a range of frequencies. Time stretching modifies the playback speed, allowing the model to learn temporal variations, and frequency scaling adjusts the spectral components to increase variability in the frequency domain.
[0073] After the augmentation process, the resulting augmented spectrogram data is ready for model training. This stage involves feeding the processed and enriched spectrograms into a deep learning model, which learns to classify or predict features from the audio data. By incorporating both time-domain processing and spectral augmentation, the workflow ensures that the model is exposed to a diverse and comprehensive set of audio scenarios. This systematic approach not only improves the model's accuracy but also enhances its generalization capability, making it robust for real-world audio recognition tasks.
[0074] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0075] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0076] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0077] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0078] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. An animal species detection and classification system for forest wild life monitoring using deep learning (100) comprising:
a data acquisition module (102) configured to receive and record raw audio signals from forest environments using one or more audio recording devices;
a pre-processing module (104) configured to segment the raw audio signals into audio slices in the time domain and convert the audio slices into spectrogram representations capturing frequency characteristics;
a hybrid data augmentation module (106) configured to enhance the spectrograms by applying frequency-domain augmentations;
a deep learning-based classification module (108) comprising one or more neural network architectures trained on the augmented dataset to detect and classify distinct animal species vocalizations;
a monitoring and output module (110) configured to generate species-specific detection and classification outputs for wildlife monitoring and ecological analysis.
2. The system (100) as claimed in claim 1, wherein the data acquisition module (102) comprises a plurality of strategically positioned audio recording devices configured to capture audio signals from multiple forest locations simultaneously.
3. The system (100) as claimed in claim 1, wherein the pre-processing module (104) is further configured to remove background noise and normalize audio signals before generating spectrogram representations.
4. The system (100) as claimed in claim 1, wherein the pre-processing module (104) generates Mel-spectrograms or Short-Time Fourier Transform (STFT) spectrograms as the frequency-domain representations.
5. The system (100) as claimed in claim 1, wherein the hybrid data augmentation module (106) applies one or more of pitch shifting, time stretching, or frequency masking to the spectrograms.
6. The system (100) as claimed in claim 1, wherein the hybrid data augmentation module (106) dynamically updates augmentation parameters based on the performance metrics of the deep learning-based classification module.
7. The system (100) as claimed in claim 1, wherein the deep learning-based classification module (108) comprises a convolutional neural network (CNN), a recurrent neural network (RNN), or a hybrid CNN-RNN architecture for species detection.
8. The system (100) as claimed in claim 1, wherein the deep learning-based classification module (108) is trained using supervised learning on labeled datasets of animal vocalizations.
9. The system (100) as claimed in claim 1, wherein the monitoring and output module (110) provides real-time alerts or notifications upon detecting specific animal species.
10. The system (100) as claimed in claim 1, wherein the monitoring and output module (110) stores historical detection data for ecological analysis and trend monitoring.