Musical Signal Type Discrimination based on Large Open Feature Sets

Schuller, Björn; Wallhoff, Frank; Arsic, D.; Rigoll, Gerhard

doi:10.1109/icme.2006.262724

Cited by 13 publications

(11 citation statements)

References 8 publications

(13 reference statements)

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“…Columns two and three show the features and classifiers used in each work, whereas column four lists the percentage of the classification process. Schuller et al have also carried out an intensive work on music/speech discrimination [43][44][45][46][47]. Their method is based on Low-Level-Descriptors 7k + hi-level-features, and they have tested different types of music signals from different databases.…”

Section: Simulation Resultsmentioning

confidence: 99%

Audio Signal Discrimination Using Evolutionary Spectrum

Al-Shoshan¹

2009

International Journal of Computers and Applications

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In this paper, a joint-distribution algorithm, mainly, the evolutionary spectrum (ES) for audio signal discrimination is proposed and discussed. The purpose of audio signal discrimination is to build two different libraries: speech library and music library, from a stream of sounds. In general, the classification algorithms can be divided into three approaches: time-domain, frequency-domain, and time-frequency domain approaches. The first two approaches were discussed thoroughly in literature; however, the third approach has not yet been tested thoroughly for this type of discrimination. As audio signals are considered non-stationary, in general, the time or frequency-domain approach alone may fail in reflecting the time-varying behaviour of such signals correctly. Therefore, in this paper, the ES is proposed to solve this type of classification problem. To show the effectiveness of the proposed algorithm, some simulation and comparisons with classical methods are done, where the Colombia SMD database is used.

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Section: Simulation Resultsmentioning

confidence: 99%

Audio Signal Discrimination Using Evolutionary Spectrum

Al-Shoshan¹

2009

International Journal of Computers and Applications

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“…by Schuller, Wallhoff, Arsic, and Rigoll (2006), Tzanetakis et al (2001) or Tzanetakis (2002), achieving remarkable accuracy at particular genres.…”

Section: Genrementioning

confidence: 97%

‘Mister D.J., Cheer Me Up!’:Musical and Textual Features for Automatic Mood Classification

Schuller

Hage

Schuller³

et al. 2010

Journal of New Music Research

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Mass consumption of large collections of digital music asks for efficient and intuitive ways of organization. In this article, a system is presented which recognizes the evoked music mood on the basis of a wide variety of features, closely sticking to real world conditions. A twodimensional mood model is discussed in which moods resemble binary values for arousal and valence and an easy and thus user-friendly method is presented through which a fuzzy seven-class mood cluster is deducted. The songs of the 'Twenty Years of MTV Europe Most Wanted' music database consisting of recorded pop music tracks serve for evaluation of three groups of features: firstly, traditional features such as rhythm and tonal features, zero crossing rate, cepstral, and MPEG-7 Low Level Descriptors for audio content are extracted. Secondly, lyrics, chord sequences, and genre data are obtained from on-line sources. Thirdly, from all these, the high-level features, musical mode, and as a novel feature, the suited ballroom dance style, are created automatically. The features selected are data-driven, and Support Vector Machines are used for classification. Prediction accuracies of 77.4% for arousal and 72.9% for valence as well as 71.8% (including neighbours) for the seven-class cluster model are obtained pre-serving realism in terms of non-prototypical music selection and feature extraction throughout.

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“…These and similar features have been applied successfully to various audio classification tasks, e.g. musical genre recognition [32], emotion recognition [33,34], and classification of non-linguistic vocalisations [35]. In order to find a set of features highly relevant for laughter classification, an automatic data-driven feature selection method called correlation-based featuresubset selection (CFS) [36] is used.…”

Section: Automatic Classification Of Laughtermentioning

confidence: 99%

On Laughter and Speech-Laugh, Based on Observations of Child-Robot Interaction

Batliner¹,

Steidl²,

Eyben³

et al. 2019

Preprint

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In this article, we study laughter found in child-robot interaction where it had not been prompted intentionally. Different types of laughter and speech-laugh are annotated and processed. In a descriptive part, we report on the position of laughter and speech-laugh in syntax and dialogue structure, and on communicative functions. In a second part, we report on automatic classification performance and on acoustic characteristics, based on extensive feature selection procedures.

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Musical Signal Type Discrimination based on Large Open Feature Sets

Cited by 13 publications

References 8 publications

Audio Signal Discrimination Using Evolutionary Spectrum

Audio Signal Discrimination Using Evolutionary Spectrum

‘Mister D.J., Cheer Me Up!’:Musical and Textual Features for Automatic Mood Classification

On Laughter and Speech-Laugh, Based on Observations of Child-Robot Interaction

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