Analysis and Improvement of Speech/Music Classification for 3GPP2 SMV Based on GMM

Song, Jang Ho; Lee, Kye-Hwan; Chang, Joon‐Hyuk; Kim, Jong Kyu; Kim, Nam Soo

doi:10.1109/lsp.2007.911184

Cited by 14 publications

(4 citation statements)

References 7 publications

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“…Research results [7][8][9] show that proper features and in turn good performance can only be obtained based on numerous experiments. Open-loop coding mode selection is essentially one kind of pattern classification.…”

Section: A Semi-open-loop Coding Mode Selection Algorithm Based mentioning

confidence: 99%

A semi-open-loop coding mode selection algorithm based on EFM and selected AMR-WB+ features

Hong

Zhao

Kuang

2009

J. Electron.(China)

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To solve the problems of the AMR-WB+ (Extended Adaptive Multi-Rate-WideBand) semi-open-loop coding mode selection algorithm, features for ACELP (Algebraic Code Excited Linear Prediction) and TCX (Transform Coded eXcitation) classification are investigated. 11 classifying features in the AMR-WB+ codec are selected and 2 novel classifying features, i.e., EFM (Energy Flatness Measurement) and stdEFM (standard deviation of EFM), are proposed. Consequently, a novel semi-open-loop mode selection algorithm based on EFM and selected AMR-WB+ features is proposed. The results of classifying test and listening test show that the performance of the novel algorithm is much better than that of the AMR-WB+ semi-open-loop coding mode selection algorithm.

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Section: A Semi-open-loop Coding Mode Selection Algorithm Based mentioning

confidence: 99%

A semi-open-loop coding mode selection algorithm based on EFM and selected AMR-WB+ features

Hong

Zhao

Kuang

2009

J. Electron.(China)

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“…Recently, further improvements in speech/music classification problems have been achieved by adopting several machine learning techniques, such as the support vector machine (SVM) [6,7], Gaussian mixture model (GMM) [8], and deep belief network (DBN) [9] for the selectable mode vocoder (SMV) codec. The enhanced voice services (EVS) speech/music classifier, which is known as the 3rd-generation partnership project (3GPP) standard speech codec for the voice-over-LTE (VoLTE) network, is also based on GMM, but its features were calculated either at a current frame or as a moving average between those in the current and the previous frames [10].…”

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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“…Energy, Entropy, RMS, Peak-to-Sidelobe ratio (PSR) from the Hilbert Envelope of the LP Residual, Normalized Autocorrelation Peak Strength (NAPS) of Zero frequency filtered signal [10], [6], [8], [7], [2], [25], [5], [29], [28] speech spectrogram. Whereas, individual notes of music have a specific onset instant, marked by a relatively large burst of energy that make its striation patterns discontinuous [21].…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Audio Features for Speech-Music Classification

Bhattacharjee,

Prasanna,

Guha

2018

Preprint

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Distinct striation patterns are observed in the spectrograms of speech and music. This motivated us to propose three novel time-frequency features for speech-music classification. These features are extracted in two stages. First, a preset number of prominent spectral peak locations are identified from the spectra of each frame. These important peak locations obtained from each frame are used to form Spectral peak sequences (SPS) for an audio interval. In second stage, these SPS are treated as time series data of frequency locations. The proposed features are extracted as periodicity, average frequency and statistical attributes of these spectral peak sequences. Speechmusic categorization is performed by learning binary classifiers on these features. We have experimented with Gaussian mixture models, support vector machine and random forest classifiers. Our proposal is validated on four datasets and benchmarked against three baseline approaches. Experimental results establish the validity of our proposal.

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Analysis and Improvement of Speech/Music Classification for 3GPP2 SMV Based on GMM

Cited by 14 publications

References 7 publications

A semi-open-loop coding mode selection algorithm based on EFM and selected AMR-WB+ features

A semi-open-loop coding mode selection algorithm based on EFM and selected AMR-WB+ features

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

Time-Frequency Audio Features for Speech-Music Classification

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