Approach For Audio Classification Research Articles

A Scalogram-Based CNN Approach for Audio Classification in Construction Sites

The automatic monitoring of activities in construction sites through the proper use of acoustic signals is a recent field of research that is currently in continuous evolution. In particular, the use of techniques based on Convolutional Neural Networks (CNNs) working on the spectrogram of the signal or its mel-scale variants was demonstrated to be quite successful. Nevertheless, the spectrogram has some limitations, which are due to the intrinsic trade-off between temporal and spectral resolutions. In order to overcome these limitations, in this paper, we propose employing the scalogramas a proper time–frequency representation of the audio signal. The scalogram is defined as the square modulus of the Continuous Wavelet Transform (CWT) and is known as a powerful tool for analyzing real-world signals. Experimental results, obtained on real-world sounds recorded in construction sites, have demonstrated the effectiveness of the proposed approach, which is able to clearly outperform most state-of-the-art solutions.

A Robust Approach for Securing Audio Classification Against Adversarial Attacks

Adversarial audio attacks can be considered as a small perturbation unperceptive to human ears that is intentionally added to an audio signal and causes a machine learning model to make mistakes. This poses a security concern about the safety of machine learning models since the adversarial attacks can fool such models toward the wrong predictions. In this paper we first review some strong adversarial attacks that may affect both audio signals and their 2D representations and evaluate the resiliency of deep learning models and support vector machines (SVM) trained on 2D audio representations such as short time Fourier transform, discrete wavelet transform (DWT) and cross recurrent plot against several state-of-the-art adversarial attacks. Next, we propose a novel approach based on pre-processed DWT representation of audio signals and SVM to secure audio systems against adversarial attacks. The proposed architecture has several preprocessing modules for generating and enhancing spectrograms including dimension reduction and smoothing. We extract features from small patches of the spectrograms using the speeded up robust feature (SURF) algorithm which are further used to transform into cluster distance distribution using the K-Means++ algorithm. Finally, SURF-generated vectors are encoded by this codebook and the resulting codewords are used for training a SVM. All these steps yield to a novel approach for audio classification that provides a good tradeoff between accuracy and resilience. Experimental results on three environmental sound datasets show the competitive performance of the proposed approach compared to the deep neural networks both in terms of accuracy and robustness against strong adversarial attacks.

An analysis of content-based classification of audio signals using a fuzzy c-means algorithm

Content-based audio signal classification into broad categories such as speech, music, or speech with noise is the first step before any further processing such as speech recognition, content-based indexing, or surveillance systems. In this paper, we propose an efficient content-based audio classification approach to classify audio signals into broad genres using a fuzzy c-means (FCM) algorithm. We analyze different characteristic features of audio signals in time, frequency, and coefficient domains and select the optimal feature vector by employing a noble analytical scoring method to each feature. We utilize an FCM-based classification scheme and apply it on the extracted normalized optimal feature vector to achieve an efficient classification result. Experimental results demonstrate that the proposed approach outperforms the existing state-of-the-art audio classification systems by more than 11% in classification performance.

A general audio classifier based on human perception motivated model

The audio channel conveys rich clues for content-based multimedia indexing. Interesting audio analysis includes, besides widely known speech recognition and speaker identification problems, speech/music segmentation, speaker gender detection, special effect recognition such as gun shots or car pursuit, and so on. All these problems can be considered as an audio classification problem which needs to generate a label from low audio signal analysis. While most audio analysis techniques in the literature are problem specific, we propose in this paper a general framework for audio classification. The proposed technique uses a perceptually motivated model of the human perception of audio classes in the sense that it makes a judicious use of certain psychophysical results and relies on a neural network for classification. In order to assess the effectiveness of the proposed approach, large experiments on several audio classification problems have been carried out, including speech/music discrimination in Radio/TV programs, gender recognition on a subset of the switchboard database, highlights detection in sports videos, and musical genre recognition. The classification accuracies of the proposed technique are comparable to those obtained by problem specific techniques while offering the basis of a general approach for audio classification.

Multigroup classification of audio signals using time-frequency parameters

The ongoing advancements in the multimedia technologies drive the need for efficient classification of the audio signals to make the content-based retrieval process more accurate and much easier from huge databases. The challenge of this task lies in an accurate extraction of signal characteristics so as to derive a strong discriminatory feature suitable for classification. In this paper, a time-frequency (TF) approach for audio classification is proposed. Audio signals are nonstationary in nature and TF approach is the best way to analyze them. The audio signals were decomposed using an adaptive TF decomposition algorithm, and the signal decomposition parameter based on octave (scaling) was used to generate a set of 42 features over three frequency bands within the auditory range. These features were analyzed using linear discriminant functions and classified into six music groups (rock, classical, country, jazz, folk and pop). Overall classification accuracies as high as 97.6 % was achieved by linear discriminant analysis of 170 audio signals.