Spectrogram based features selection using multiple kernel learning for speech/music discrimination

This paper presents an efficient approach for classification of speech signals as reverberant or not. The reverberation is a severe effect encountered in closed room. So, it may affect subsequent processes and deteriorate speech processing system performance. The spectrograms are utilized as images generated from speech signals to be classified with deep convolutional neural networks. Spectrogram and MFCC are used as features to be classified with Long Short Term Recurrent Neural Network (LSTM RNN). Two models are presented and compared. Simulation results up to 100% classification accuracy are obtained. This can help in perform an initial step in any speech processing system that comprises quality level classification.

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“…Where ᴪ is the STFT operator, X(τ,k) is the STFT of the signal x(n), w(n) is the window and S(τ,k) is the spectrogram [13], [14].…”

Section: Reverberation Phenomenamentioning

confidence: 99%

Quality Evaluation of Reverberant Speech Based on Deep Learning

El-Moneim

Saied

Nassar

et al. 2020

Menoufia Journal of Electronic Engineering Research

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“…Nilufar et al [54] use wavelet packet decomposition [55], an extension of wavelet transform that includes more signal filters, for robust speech and music discrimination. This technique is applied to the spectrogram to transform it into different subbands containing texture information.…”

Section: Other Time-frequency Representationsmentioning

confidence: 99%

An overview of applications and advancements in automatic sound recognition

Sharan

Moir

2016

Neurocomputing

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a b s t r a c tAutomatic sound recognition (ASR) has attracted increased and wide ranging interests in recent years. In this paper, we carry out a review of some important contributions in ASR techniques, mainly over the last one and a half decades. Similar to speech recognition systems, the robustness of an ASR system largely depends on the choice of feature(s) and classifier(s). We take a wider perspective in providing an overview of the features and classifiers used in ASR systems starting from early works in content-based audio classification to more recent developments in applications such as sound event recognition, audio surveillance, and environmental sound recognition. We also review techniques that have been utilized in noise robust sound recognition systems and feature optimization methods. Finally, some of the less commonly known applications of ASR are discussed.

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