Forest Sound Classification Dataset: FSC22 (original) (raw)
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CnnSound: Convolutional Neural Networks for the Classification of Environmental Sounds
2020 The 4th International Conference on Advances in Artificial Intelligence, 2020
The classification of environmental sounds (ESC) has been increasingly studied in recent years. The main reason is that environmental sounds are part of our daily life, and associating them with our environment that we live in is important in several aspects as ESC is used in areas such as managing smart cities, determining location from environmental sounds, surveillance systems, machine hearing, environment monitoring. The ESC is however more difficult than other sounds because there are too many parameters that generate background noise in the ESC, which makes the sound more difficult to model and classify. The main aim of this study is therefore to develop more robust convolution neural networks architecture (CNN). For this purpose, 150 different CNN-based models were designed by changing the number of layers and values of their tuning parameters used in the layers. In order to test the accuracy of the models, the Urbansound8k environmental sound database was used. The sounds in...
Deep Convolutional Neural Networks for Environmental Sound Classification
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
We propose a model to classify environmental sounds such as People Sounds, Vehicles Sounds, Siren Sounds, Horn, Engine Sounds. We perform Data Augmentation techniques to extract best features from the given audio to classify which class of sound. Our deep convolutional neural network architecture uses stacked convolutional and pooling layers to extract highlevel feature representations from spectrogram-like features from the given input.
The adoption of the environmental sound classification (ESC) tasks increases very rapidly over recent years due to its broad range of applications in our daily routine life. ESC is also known as Sound Event Recognition (SER) which involves the context of recognizing the audio stream, related to various environmental sounds. Some frequent and common aspects like non-uniform distance between acoustic source and microphone, the difference in the framework, presence of numerous sounds sources in audio recordings and overlapping various sound events make this ESC problem much complex and complicated. This study is to employ deep convolutional neural networks (DCNN) with regularization and data enhancement with basic audio features that have verified to be efficient on ESC tasks. In this study, the performance of DCNN with max-pooling (Model-1) and without max-pooling (Model-2) function are examined. Three audio attribute extraction techniques, Mel spectrogram (Mel), Mel Frequency Cepstral Coefficient (MFCC) and Log-Mel, are considered for the ESC-10, ESC-50, and Urban sound (US8K) datasets. Furthermore, to avoid the risk of overfitting due to limited numbers of data, this study also introduces offline data augmentation techniques to enhance the used datasets with a combination of L2 regularization. The performance evaluation illustrates that the best accuracy attained by the proposed DCNN without max-pooling function (Model-2) and using Log-Mel audio feature extraction on those augmented datasets. For ESC-10, ESC-50 and US8K, the highest achieved accuracies are 94.94%, 89.28%, and 95.37% respectively. The experimental results show that the proposed approach can accomplish the best performance on environment sound classification problems.
Deep Learning For Natural Sound Classification
2019
Nowadays, it is very common to use sensors for controlling the population of different animal species in a natural environment. A large number of sensors can be deployed in wide areas and they will capture information relentlessly, producing a huge amount of data. However, analysing the collected data by humans is a big challenge and for that reason, it is necessary to develop automated technologies in order to help experts on that task. Within this context, we present an automatic system to detect and classify sounds, especially those generated by birds and insects among other sounds that can be heard in a natural environment. For the development of the system, it has been necessary to generate a sound database. The recorded database consists of field recordings in three different Natural Parks, with sounds of several bird and insect species, as well as background noises. The automated system employs state of the art neural networks for detecting and classifying sound frames. Exper...
Deep Learning-based Environmental Sound Classification Using Feature Fusion and Data Enhancement
Computers, Materials & Continua
Environmental sound classification (ESC) involves the process of distinguishing an audio stream associated with numerous environmental sounds. Some common aspects such as the framework difference, overlapping of different sound events, and the presence of various sound sources during recording make the ESC task much more complicated and complex. This research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation resources. In this research, the performance of transformer and convolutional neural networks (CNN) are investigated. Seven audio features, chromagram, Mel-spectrogram, tonnetz, Mel-Frequency Cepstral Coefficients (MFCCs), delta MFCCs, delta-delta MFCCs and spectral contrast, are extracted from the UrbanSound8K, ESC-50, and ESC-10, databases. Moreover, this research also employed three data enhancement methods, namely, white noise, pitch tuning, and time stretch to reduce the risk of overfitting issue due to the limited audio clips. The evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on enhanced database. For UrbanSound8K, ESC-50, and ESC-10, the highest attained accuracies are 0.98, 0.94, and 0.97 respectively. The experimental results reveal that the proposed technique can achieve the best performance for ESC problems.
The Application and Improvement of Deep Neural Networks in Environmental Sound Recognition
Applied Sciences, 2020
Neural networks have achieved great results in sound recognition, and many different kinds of acoustic features have been tried as the training input for the network. However, there is still doubt about whether a neural network can efficiently extract features from the raw audio signal input. This study improved the raw-signal-input network from other researches using deeper network architectures. The raw signals could be better analyzed in the proposed network. We also presented a discussion of several kinds of network settings, and with the spectrogram-like conversion, our network could reach an accuracy of 73.55% in the open-audio-dataset “Dataset for Environmental Sound Classification 50” (ESC50). This study also proposed a network architecture that could combine different kinds of network feeds with different features. With the help of global pooling, a flexible fusion way was integrated into the network. Our experiment successfully combined two different networks with differen...
A new pyramidal concatenated CNN approach for environmental sound classification
Applied Acoustics, 2020
Recently, there has been an incremental interest on Environmental Sound Classification (ESC), which is an important topic of the non-speech audio classification task. A novel approach, which is based on deep Convolutional Neural Networks (CNN), is proposed in this study. The proposed approach covers a bunch of stages such as pre-processing, deep learning based feature extraction, feature concatenation, feature reduction and classification, respectively. In the first stage, the input sound signals are denoised and are converted into sound images by using the Sort Time Fourier Transform (STFT) method. After sound images are formed, pre-trained CNN models are used for deep feature extraction. In this stage, VGG16, VGG19 and DenseNet201 models are considered. The feature extraction is performed in a pyramidal fashion which makes the dimension of the feature vector quite large. For both dimension reduction and the determination of the most efficient features, a feature selection mechanism is considered after feature concatenation stage. In the last stage of the proposed method, a Support Vector Machines (SVM) classifier is used. The efficiency of the proposed method is calculated on various ESC datasets such as ESC 10, ESC 50 and UrbanSound8K, respectively. The experimental works show that the proposed method produced 94.8%, 81.4% and 78.14% accuracy scores for ESC-10, ESC-50 and UrbanSound8K datasets. The obtained results are also compared with the state-of-the art methods achievements.
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Humans are able to identify a large number of environmental sounds and categorise them according to high-level semantic categories, e.g. urban sounds or music. They are also capable of generalising from past experience to new sounds when applying these categories. In this paper we report on the creation of a data set that is structured according to the top-level of a taxonomy derived from human judgements and the design of an associated machine learning challenge, in which strong generalisation abilities are required to be successful. We introduce a baseline classification system, a deep convolutional network, which showed strong performance with an average accuracy on the evaluation data of 80.8%. The result is discussed in the light of two alternative explanations: An unlikely accidental category bias in the sound recordings or a more plausible true acoustic grounding of the high-level categories.
End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network
Expert Systems with Applications
In this paper, we present an end-to-end approach for environmental sound classification based on a 1D Convolution Neural Network (CNN) that learns a representation directly from the audio signal. Several convolutional layers are used to capture the signal's fine time structure and learn diverse filters that are relevant to the classification task. The proposed approach can deal with audio signals of any length as it splits the signal into overlapped frames using a sliding window. Different architectures considering several input sizes are evaluated, including the initialization of the first convolutional layer with a Gammatone filterbank that models the human auditory filter response in the cochlea. The performance of the proposed end-to-end approach in classifying environmental sounds was assessed on the UrbanSound8k dataset and the experimental results have shown that it achieves 89% of mean accuracy. Therefore, the propose approach outperforms most of the state-of-the-art approaches that use handcrafted features or 2D representations as input. Furthermore, the proposed approach has a small number of parameters compared to other architectures found in the literature, which reduces the amount of data required for training.