Auditory Segmentation Based on Onset and Offset Analysis

Hu, Guoning; Wang, DeLiang

doi:10.1109/tasl.2006.881700

Cited by 113 publications

(77 citation statements)

References 23 publications

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“…The note onset can be identified by sudden changes in the energy profile [25]. In the case of gamelan note attacks, this increasing energy will be very sharp.…”

Section: Segmentation Modulementioning

confidence: 99%

Gamelan instrument sound recognition using spectral and facial features of the first harmonic frequency

Tjahyanto

Wulandari

Suprapto

et al. 2015

Acoust. Sci. ^|^ Tech.

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Principal component and spectral-based feature sets were applied to the recognition of gamelan instrument sounds using support vector machines (SVMs). The principal components were calculated on the basis of a segmented scalogram from the first harmonic frequency of the gamelan recordings. The segmented scalogram is assumed as a ''facial image'' of the gamelan instrument sound in a frontal pose, neutral expression, and normal lighting. The scalogram was computed from the gamelan sound signal using a continuous wavelet transform (CWT). The performance and contribution of the principal component and spectral-based features were compared using an F-measure. For the training phase, the feature sets were extracted from isolated tones that were recorded over the entire frequency range of four gamelan instruments (demung, saron, peking, and bonang families). Using 90%/10% splits between the training and validating data sets, model classifiers were constructed from the radial basis function (RBF) kernel SVM. The classifiers are composed of 28 separate One-AgainstOne multiclass classifiers. The experiment showed that the spectral-based feature set shows an average F-measure of 74.05% and the appearance-based feature yields 71.87%. For saron-only note tracking, the spectral-based feature set had an F-measure of 83.79%, higher than the demung-only note tracking, which yielded 63.89%.

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“…The note onset can be identified by sudden changes in the energy profile [25]. In the case of gamelan note attacks, this increasing energy will be very sharp.…”

Section: Segmentation Modulementioning

confidence: 99%

Gamelan instrument sound recognition using spectral and facial features of the first harmonic frequency

Tjahyanto

Wulandari

Suprapto

et al. 2015

Acoust. Sci. ^|^ Tech.

View full text Add to dashboard Cite

show abstract

“…The typical methods are based on the similarity measures of audio frames [1] and the techniques using the comparison of the signal models [2]. An analysis of the onsets found in audio data is the basis of some approaches [3], [4]. In the [5], a segmentation based on an analysis of the self-similarity matrix by computing the inter-frame spectral similarity is presented.…”

Section: Related Workmentioning

confidence: 99%

Change Point Determination in Audio Data Using Auditory Features

Maka¹

2015

International Journal of Electronics and Telecommunications

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Abstract-The study is aimed to investigate the properties of auditory-based features for audio change point detection process. In the performed analysis, two popular techniques have been used: a metric-based approach and the ∆BIC scheme. The efficiency of the change point detection process depends on the type and size of the feature space. Therefore, we have compared two auditory-based feature sets (MFCC and GTEAD) in both change point detection schemes. We have proposed a new technique based on multiscale analysis to determine the content change in the audio data. The comparison of the two typical change point detection techniques with two different feature spaces has been performed on the set of acoustical scenes with single change point. As the results show, the accuracy of the detected positions depends on the feature type, feature space dimensionality, detection technique and the type of audio data. In case of the ∆BIC approach, the better accuracy has been obtained for MFCC feature space in the most cases. However, the change point detection with this feature results in a lower detection ratio in comparison to the GTEAD feature. Using the same criteria as for ∆BIC, the proposed multiscale metric-based technique has been executed. In such case, the use of the GTEAD feature space has led to better accuracy. We have shown that the proposed multiscale change point detection scheme is competitive to the ∆BIC scheme with the MFCC feature space.

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“…Only those units with sufficiently high cross-channel correlation or envelope cross-channel correlation will be iteratively merged into segments. For unvoiced intervals, a multiscale onset/offset analysis [13] is applied to form segments, which detects sudden intensity increases (onsets) and decreases (offsets). This analysis is appropriate for segmenting unvoiced speech.…”

Section: Auditory Segmentationmentioning

confidence: 99%

An SVM based classification approach to speech separation

Han

Wang

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Monaural speech separation is a very challenging task. CASAbased systems utilize acoustic features to produce a time-frequency (T-F) mask. In this study, we propose a classification approach to monaural separation problem. Our feature set consists of pitchbased features and amplitude modulation spectrum features, which can discriminate both voiced and unvoiced speech from nonspeech interference. We employ support vector machines (SVMs) followed by a re-thresholding method to classify each T-F unit as either targetdominated or interference-dominated. An auditory segmentation stage is then utilized to improve SVM-generated results. Systematic evaluations show that our approach produces high quality binary masks and outperforms a previous system in terms of classification accuracy.

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Auditory Segmentation Based on Onset and Offset Analysis

Cited by 113 publications

References 23 publications

Gamelan instrument sound recognition using spectral and facial features of the first harmonic frequency

Gamelan instrument sound recognition using spectral and facial features of the first harmonic frequency

Change Point Determination in Audio Data Using Auditory Features

An SVM based classification approach to speech separation

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