Bird Species Identification Using Spectrogram Based on Multi-Channel Fusion of DCNNs

Abstract

Deep convolutional neural networks (DCNNs) have achieved breakthrough performance on bird species identification using a spectrogram of bird vocalization. Aiming at the imbalance of the bird vocalization dataset, a single feature identification model (SFIM) with residual blocks and modified, weighted, cross-entropy function was proposed. To further improve the identification accuracy, two multi-channel fusion methods were built with three SFIMs. One of these fused the outputs of the feature extraction parts of three SFIMs (feature fusion mode), the other fused the outputs of the classifiers of three SFIMs (result fusion mode). The SFIMs were trained with three different kinds of spectrograms, which were calculated through short-time Fourier transform, mel-frequency cepstrum transform and chirplet transform, respectively. To overcome the shortage of the huge number of trainable model parameters, transfer learning was used in the multi-channel models. Using our own vocalization dataset as a sample set, it is found that the result fusion mode model outperforms the other proposed models, the best mean average precision (MAP) reaches 0.914. Choosing three durations of spectrograms, 100 ms, 300 ms and 500 ms for comparison, the results reveal that the 300 ms duration is the best for our own dataset. The duration is suggested to be determined based on the duration distribution of bird syllables. As for the performance with the training dataset of BirdCLEF2019, the highest classification mean average precision (cmAP) reached 0.135, which means the proposed model has certain generalization ability.

A Task-Adaptive Parameter Transformation Scheme for Model-Agnostic-Meta-Learning-Based Few-Shot Animal Sound Classification

“…On the other hand, Zhang et al proposed a method that can achieve outstanding bird sound classification accuracy based on deep CNNs (DCNNs) [4,13]. Specifically, they calculated spectrograms of the short-time Fourier transform, Mel-frequency transform, and Chirplet transform for animal sounds, constructed individual DCNN models for each spectrogram, and predicted bird species by combining the features from the DCNN models.…”

“…ASC is particularly useful when visual identification is challenging, such as regarding small, nocturnal, and camouflaged animals [2]. Recently, deep learning-based models, such as convolutional neural networks (CNNs), have demonstrated superior performance in ASC as well as in other signal processing applications [3,4].…”

DualDiscWaveGAN-Based Data Augmentation Scheme for Animal Sound Classification

“…ASC is a particularly useful tool for cases where visual identification is difficult, such as small animals, nocturnal animals, and camouflaged animals. Recently, deep learning-based models such as convolutional neural networks (CNNs) have been widely used in ASC [ 2 , 3 ] as well as other signal processing applications. Although they show excellent classification performance by using temporal and frequency characteristics suitable for sound classification, their performance is greatly affected by the quality and quantity of the animal sound data used for learning.…”

An Efficient Model for a Vast Number of Bird Species Identification Based on Acoustic Features

“…Cross−modalities method is another research direction in identifying bird calls. Zhang et al [ 46 ] combined the STFT−spectrogram, Mel−spectrogram, and Chirplet−spectrogram as input features and used CNN to identify 18 bird species. The results demonstrated that the best mAP reached 91.4%.…”