A robust speech/music discriminator for switched audio coding

Fuchs, Guillaume

doi:10.1109/eusipco.2015.7362447

Cited by 14 publications

(3 citation statements)

References 10 publications

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“…Speech/Music discrimination is a task of binary classification where the incoming audio signals will be either classified as speech or music. Conventional classifiers like SVM, GMM, KNN and ANN have been widely explored in this domain with promising results [20,2,16,5]. Hence, the proposed features were evaluated for its efficiency on SVM and k-NN classifiers for a comparative analysis.…”

Section: Classifiersmentioning

confidence: 99%

Hilbert spectrum based features for speech/music classification

2022

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Automatic Speech/Music classification uses different signal processing techniques to categorize multimedia content into different classes. The proposed work explores Hilbert Spectrum (HS) obtained from different AM-FM components of an audio signal, also called Intrinsic Mode Functions (IMFs) to classify an incoming audio signal into speech/music signal. The HS is a twodimensional representation of instantaneous energies (IE) and instantaneous frequencies (IF) obtained using Hilbert Transform of the IMFs. This HS is further processed using Mel-filter bank and Discrete Cosine Transform (DCT) to generate novel IF and Instantaneous Amplitude (IA) based cepstral features. Validations of the results were done using three databases-Slaney Database, GTZAN and MUSAN database. To evaluate the general applicability of the proposed features, extensive experiments were conducted on different combination of audio files from S&S, GTZAN and MUSAN database and promising results are achieved. Finally, performance of the system is compared with performance of existing cepstral features and previous works in this domain.

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Section: Classifiersmentioning

confidence: 99%

Hilbert spectrum based features for speech/music classification

2022

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“…Recently, a number of adaptive multi-rate (AMR) voice codecs have been proposed to efficiently utilize the limited bandwidth resources available [1][2][3]. Precise determination of speech/music classification is quite necessary, as different bit rate allocations for the correct input/output formats affect the voice characteristic of these adaptive multi-rate voice codecs [4,5].…”

Section: Introductionmentioning

confidence: 99%

Improvement of Speech/Music Classification for 3GPP EVS Based on LSTM

Kang

Lee

2018

Symmetry

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The competition of speech recognition technology related to smartphones is now getting into full swing with the widespread internet of thing (IoT) devices. For robust speech recognition, it is necessary to detect speech signals in various acoustic environments. Speech/music classification that facilitates optimized signal processing from classification results has been extensively adapted as an essential part of various electronics applications, such as multi-rate audio codecs, automatic speech recognition, and multimedia document indexing. In this paper, we propose a new technique to improve robustness of a speech/music classifier for an enhanced voice service (EVS) codec adopted as a voice-over-LTE (VoLTE) speech codec using long short-term memory (LSTM). For effective speech/music classification, feature vectors implemented with the LSTM are chosen from the features of the EVS. To overcome the diversity of music data, a large scale of data is used for learning. Experiments show that LSTM-based speech/music classification provides better results than the conventional EVS speech/music classification algorithm in various conditions and types of speech/music data, especially at lower signal-to-noise ratio (SNR) than conventional EVS algorithm.

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“…Recently, a number of adaptive multi-rate (AMR) voice codecs have been proposed to efficiently utilize of limited bandwidth resources available [1][2][3]. Precise determination of speech/music classification is quite necessary, as different bit rate allocations for the correct input/output formats affect the voice characteristic of these adaptive multi-rate voice codecs [4,5].…”

Section: Introductionmentioning

confidence: 99%

Improvement of Speech/Music Classification for 3GPP EVS Based on LSTM

Kang¹,

Lee²

2018

Preprint

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Speech/music classification that facilitates optimized signal processing from classification results has been extensively adapted as an essential part of various electronics applications, such as multi-rate audio codecs, automatic speech recognition, and multimedia document indexing. In this paper, a new technique to improve the robustness of speech/music classifier for 3GPP enhanced voice service (EVS) using long short-term memory (LSTM) is proposed. For effective speech/music classification, feature vectors implemented with the LSTM are chosen from the features of the EVS. Experiments show that LSTM-based speech/music classification produces better results than conventional EVS under a variety of conditions and types of speech/music data.

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A robust speech/music discriminator for switched audio coding

Cited by 14 publications

References 10 publications

Hilbert spectrum based features for speech/music classification

Hilbert spectrum based features for speech/music classification

Improvement of Speech/Music Classification for 3GPP EVS Based on LSTM

Improvement of Speech/Music Classification for 3GPP EVS Based on LSTM

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