SVM Candidates and Sparse Representation for Bird Identification

2017

The LifeCLEF challenge BirdCLEF offers a large-scale proving ground for system-oriented evaluation of bird species identification based on audio recordings of their sounds. One of its strengths is that it uses data collected through Xeno-canto, the worldwide community of bird sound recordists. This ensures that BirdCLEF is close to the conditions of real-world application, in particular with regard to the number of species in the training set (1500). The main novelty of the 2017 edition of BirdCLEF was the inclusion of soundscape recordings containing time-coded bird species annotations in addition to the usual Xeno-canto recordings that focus on a single foreground species. This paper reports an overview of the systems developed by the five participating research groups, the methodology of the evaluation of their performance, and an analysis and discussion of the results obtained.

IJITCE, 2024

Bird species identification through vocalization analysis is a growing field within bioacoustics and machine learning. The goal is to identify bird species by analyzing their unique vocal traits, such as calls and songs, which vary significantly across species. Audio data collected from natural environments is processed using machine learning algorithms to classify species based on these vocal characteristics. Recent advances in deep learning and signal processing, such as spectrogram analysis, have enhanced the precision of bird vocalization classification. Techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed to differentiate between species by analyzing the extracted vocal features. This method offers a non-invasive way to monitor bird populations and study behaviors, aiding conservation and ecological research. Additionally, real-time voice-based classification systems allow for rapid species identification, improving field studies. However, challenges such as variability in recording conditions, background noise, and the need for large, well-labeled datasets complicate the classification process. Despite these challenges, the integration of machine learning with vocalization analysis holds great promise for advancing bird conservation and ecological studies.

Lecture Notes in Computer Science, 2021

Bird species identification is a relevant and time-consuming task for ornithologists and ecologists. With growing amounts of audio annotated data, automatic bird classification using machine learning techniques is an important trend in the scientific community. Analyzing bird behavior and population trends helps detect other organisms in the environment and is an important problem in ecology. Bird populations react quickly to environmental changes, which makes their real time counting and tracking challenging and very useful. A reliable methodology that automatically identifies bird species from audio would therefore be a valuable tool for the experts in different scientific and applicational domains. The goal of this work is to propose a methodology able to identify bird species by its chirp. In this paper we explore deep learning techniques that are being used in this domain, such as Convolutional Neural Networks and Recurrent Neural Networks to classify the data. In deep learning, audio problems are commonly approached by converting them into images using audio feature extraction techniques such as Mel Spectrograms and Mel Frequency Cepstral Coefficients. We propose and test multiple deep learning and feature extraction combinations in order to find the most suitable approach to this problem.

IRJET, 2021

As an area of interest in ecology is monitoring animal populations to better understand their behaviour, biodiversity and population dynamics. Acoustically active birds can be automatically based on their sounds and a particularly useful ecological indicator is the bird, as it responds quickly to changes in its environment. This can be done by using the method that is only for purely audio-based bird species recognition through the application of support vector machines. The deep residual neural network that has to be trained on one of the largest bird song data set in the world so as to classify bird species based on their song or sound. The existing systems on this subject has various disadvantages in term of cost, efficiency or the maintenance of their records or the data collected for the longer period of time. The proposed technique is followed by extracting cepstral features on mel scale of each audio recording from the collected standard database. Extracted mel frequency of cepstral coefficients formed a feature matrix. This feature matrix is then trained and tested for efficient recognition of audio events from audio test signals. Once the bird species is identified then it is even possible to get few features regarding that bird using this system.

— Automatic identification of bird species based on the chirping sounds of birds was experimented using feature extraction method and classification based on support vector machines (SVMs). The proposed technique followed the extraction of cepstral features on mel scale of each audio recording from the collected standard database. Extracted mel frequency cepstral coefficients (MFCCs) formed a feature matrix. This feature matrix was then trained and tested for efficient recognition of audio events from audio test signals. The classifier achieved upto 89.4% accuracy on a data set containing four species.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021

The objective is naturally recognize which types of bird is available in a sound data set utilizing regulated learning. Contriving successful calculations for bird species order is a fundamental advance toward separating valuable natural information from accounts gathered in the field. Here Naïve Bayes calculation to characterize bird voices into various species dependent on 265 highlights removed from the chipping sound of birds. The difficulties in this undertaking included memory the executives, the quantity of bird species for the machine perceive, and the jumble in signal-to-clamor proportion between the preparation and the testing sets. So to settle this difficulties we utilized Naïve Bayes calculation from this we got great precision in it. The calculation Naive Bayes got 91.58% exactness.

arXiv (Cornell University), 2018

Reliable identification of bird species in recorded audio files would be a transformative tool for researchers, conservation biologists, and birders. In recent years, artificial neural networks have greatly improved the detection quality of machine learning systems for bird species recognition. We present a baseline system using convolutional neural networks. We publish our code base as reference for participants in the 2018 LifeCLEF bird identification task and discuss our experiments and potential improvements.

PeerJ

Automated acoustic recognition of birds is considered an important technology in support of biodiversity monitoring and biodiversity conservation activities. These activities require processing large amounts of soundscape recordings. Typically, recordings are transformed to a number of acoustic features, and a machine learning method is used to build models and recognize the sound events of interest. The main problem is the scalability of data processing, either for developing models or for processing recordings made over long time periods. In those cases, the processing time and resources required might become prohibitive for the average user. To address this problem, we evaluated the applicability of three data reduction methods. These methods were applied to a series of acoustic feature vectors as an additional postprocessing step, which aims to reduce the computational demand during training. The experimental results obtained using Mel-frequency cepstral coefficients (MFCCs) and...

This paper combines both approaches for bird species identification by extracting visual features from bird images and acoustic features from bird calls. Some bird species are rarely found in certain regions, and it's difficult to track them if done the prediction is difficult. In order to withstand this issue, we've come across a significant and easier way to recognize these bird species based on their features. We've used BirdCLEF 2022 dataset for the audio segment and the BIRDS 400 dataset for the image segment for the training and testing parts. Since among most of the approaches, we have studied CNN as vanquishing, therefore we've used CNN for both visual as well as acoustic identification. CNN is the strong assemblage of ML which has proven efficient in image processing. Our project has become attractive because of the techniques and recent advances within the domain of deep learning. With novel preprocessing and data augmentation methods, we train a convolutional neural network on the largest public obtainable dataset. By establishing a dataset and using the rule of similarity comparison algorithms, our system can provide the best results. By using our system, everyone will simply be able to determine the species of the particular bird which they provide image/audio or both as input.

2011 IEEE International Symposium on Multimedia, 2011

In this paper we focus on the automatic identification of bird species from their audio recorded song. Bird monitoring is important to perform several tasks, such as to evaluate the quality of their living environment or to monitor dangerous situations to planes caused by birds near airports. We deal with the bird species identification problem using signal processing and machine learning techniques. First, features are extracted from the bird recorded songs using specific audio treatment; next the problem is performed according to a classical machine learning scenario, where a labeled database of previously known bird songs are employed to create a decision procedure that is used to predict the species of a new bird song. Experiments are conducted in a dataset of recorded songs of bird species which appear in a specific region. The experimental results compare the performance obtained in different situations, encompassing the complete audio signals, as recorded in the field, and short audio segments (pulses) obtained from the signals by a split procedure. The influence of the number of classes (bird species) in the identification accuracy is also evaluated.