Speech and Singing Discrimination for Audio Data Indexing

Tsai, Wei-Ho; Ma, Cin-Hao

doi:10.1109/bigdata.congress.2014.138

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Some works combine short-term features related with the spectral envelope and long-term features related with prosody to train Gaussian Mixture Models (GMM) and distinguish speech and singing. In [1], the authors found that short-term features work better for segments shorter than 1 s and pitch related features obtain the best results for segments longer than 1 s. On the contrary, the work in [13] finds that spectral features work better than pitch based ones when used alone in their database composed of segments between 17 and 26 s. Regardless, they obtained the best results when combining both types of features. In [14], a large set of 276 attributes related with spectral envelope, pitch, harmonic to noise ratio and other characteristics and a ensemble of classifiers are proposed to classify singing voice, speech and polyphonic music and get very good results.…”

Section: Introductionmentioning

confidence: 99%

Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification

et al. 2019

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Speech and singing voice discrimination is an important task in the speech processing area given that each type of voice requires different information retrieval and signal processing techniques. This discrimination task is hard even for humans depending on the length of voice segments. In this article, we present an automatic speech and singing voice classification method using pitch parameters derived from musical note information and f 0 stability analysis. We applied our method to a database containing speech and a capella singing and compared the results with other discrimination techniques based on information derived from pitch and spectral envelope. Our method obtains good results discriminating both voice types, is efficient, has good generalisation capabilities and is computationally fast. In the process, we have also created a note detection algorithm with parametric control of the characteristics of the notes it detects. We compared the agreement of this algorithm with a state-of-the-art note detection algorithm and performed an experiment that proves that speech and singing discrimination parameters can represent generic information about the music style of the singing voice.

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Section: Introductionmentioning

confidence: 99%

Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification

et al. 2019

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“…The second approach uses long-term features for classification. In previous works, distribution of pitch values [9], pitch parameters [10] and note grammars [11], [12] are used. These long term features can be calculated for the whole file or for a window that slides through the audio file.…”

Section: Introductionmentioning

confidence: 99%

Speech and monophonic singing segmentation using pitch parameters

Sarasola¹,

Navas²,

Tavarez³

et al. 2018

IberSPEECH 2018

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In this paper we present a novel method for automatic segmentation of speech and monophonic singing voice based only on two parameters derived from pitch: proportion of voiced segments and percentage of pitch labelled as a musical note. First, voice is located in audio files using a GMM-HMM based VAD and pitch is calculated. Using the pitch curve, automatic musical note labelling is made applying stable value sequence search. Then pitch features extracted from each voice island are classified with Support Vector Machines. Our corpus consists in recordings of live sung poetry sessions where audio files contain both singing and speech voices. The proposed system has been compared with other speech/singing discrimination systems with good results.

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