Speech / music classification using speech-specific features

Khonglah, Banriskhem K.; Prasanna, S. R. Mahadeva

doi:10.1016/j.dsp.2015.09.005

Cited by 41 publications

(18 citation statements)

References 19 publications

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“…In 2016, Khonglah and Mahadeva Prasanna (2016) have suggested the speech-specific features for the classification of music and speech. The extracted features have shown the vocal tract type, source of excitation and syllabic rate.…”

Section: Literature Review 21 Related Workmentioning

confidence: 99%

“…Finally, the proposed system was tested with different classification algorithms and showed promising results. (Costa et al, 2012;Doudpota et al, 2013;Fu et al, 2011;Herremans et al, 2015;Liu et al, 2014;Ren et al, 2015), logistic regression (Herremans et al, 2015), if-then rule set (Herremans et al, 2015), decision tree (Herremans et al, 2015), threshold-based classifier (Khonglah and Mahadeva Prasanna, 2016) and k-NN classifier (Chen and Wang, 2013) (Costa et al, 2012;Doudpota et al, 2013;Fu et al, 2011;Herremans et al, 2015;Liu et al, 2014;Ren et al, 2015), need to find the best threshold values (Khonglah and Mahadeva Prasanna, 2016), complex calculation (Chen and Wang, 2013;Herremans et al, 2015), computationally expensive (Chen and Wang, 2013). Moreover, the reported papers varied with feature extraction.…”

Section: Literature Review 21 Related Workmentioning

confidence: 99%

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Recognizing ragas of Carnatic genre using advanced intelligence: a classification system for Indian music

Kumaraswamy

Poonacha

2020

DTA

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PurposeIn general, Indian Classical Music (ICM) is classified into two: Carnatic and Hindustani. Even though, both the music formats have a similar foundation, the way of presentation is varied in many manners. The fundamental components of ICM are raga and taala. Taala basically represents the rhythmic patterns or beats (Dandawate et al., 2015; Kirthika and Chattamvelli, 2012). Raga is determined from the flow of swaras (notes), which is denoted as the wider terminology. The raga is defined based on some vital factors such as swaras, aarohana-avarohna and typical phrases. Technically, the fundamental frequency is swara, which is definite through duration. Moreover, there are many other problems for automatic raga recognition model. Thus, in this work, raga is recognized without utilizing explicit note series information and necessary to adopt an efficient classification model.Design/methodology/approachThis paper proposes an efficient raga identification system through which music of Carnatic genre can be effectively recognized. This paper also proposes an adaptive classifier based on NN in which the feature set is used for learning. The adaptive classifier exploits advanced metaheuristic-based learning algorithm to get the knowledge of the extracted feature set. Since the learning algorithm plays a crucial role in defining the precision of the raga recognition, this model prefers to use the GWO.FindingsThrough the performance analysis, it is witnessed that the accuracy of proposed model is 16.6% better than NN with LM, NN with GD and NN with FF respectively, 14.7% better than NN with PSO. Specificity measure of the proposed model is 19.6, 24.0, 13.5 and 17.5% superior to NN with LM, NN with GD, NN with FF and NN with PSO, respectively. NPV of the proposed model is 19.6, 24, 13.5 and 17.5% better than NN with LM, NN with GD, NN with FF and NN with PSO, respectively. Thus it has proven that the proposed model has provided the best result than other conventional classification methods.Originality/valueThis paper intends to propose an efficient raga identification system through which music of Carnatic genre can be effectively recognized. This paper also proposes an adaptive classifier based on NN.

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Section: Literature Review 21 Related Workmentioning

confidence: 99%

Section: Literature Review 21 Related Workmentioning

confidence: 99%

Recognizing ragas of Carnatic genre using advanced intelligence: a classification system for Indian music

Kumaraswamy

Poonacha

2020

DTA

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“…The GMM model was used in studies related to music data processing and music genre classification [46,51]. Over the last few years and so far, GMM has continued to be used for music genre recognition, indexing, and retrieval of music [52][53][54][55][56][57][58][59][60]. This is because the GMM model is characterized by the parameters related averages and variance of data also allow modeling of data distribution with optional precision.…”

Section: Gaussian Mixture Modelmentioning

confidence: 99%

Some New Results on Automatic Identification of Vietnamese Folk Songs Cheo and Quanho

Thành¹,

Loan²,

Quang³

2020

JCC

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Vietnamese folk songs are very rich in genre and content. Identifying Vietnamese folk tunes will contribute to the storage and search for information about these tunes automatically. The paper will present an overview of the classification of music genres that have been performed in Vietnam and abroad. For two types of very popular folk songs of Vietnam such as Cheo and Quan ho, the paper describes the dataset and GMM (Gaussian Mixture Model) to perform the experiments on identifying some of these folk songs. The GMM used for experiment with 4 sets of parameters containing MFCC (Mel Frequency Cepstral Coefficients), energy, first derivative and second derivative of MFCC and energy, tempo, intensity, and fundamental frequency. The results showed that the parameters added to the MFCCs contributed significantly to the improvement of the identification accuracy with the appropriate values of Gaussian component number M. Our experiments also showed that, on average, the length of the excerpts was only 29.63% of the whole song for Cheo and 38.1% of the whole song for Quan ho, the identification rate was only 3.1% and 2.33% less than the whole song for Cheo and Quan ho respectively.

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“…Therefore, when dealing with recordings that contain both types of speech, some tool must be designed to identify them in order to apply the technique suitable for each type of voice. Many works have addressed the problem of speech and music discrimination [6][7][8][9], but these techniques are not directly applicable in the case of a capella singing because they exploit the presence of music.…”

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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Speech / music classification using speech-specific features

Cited by 41 publications

References 19 publications

Recognizing ragas of Carnatic genre using advanced intelligence: a classification system for Indian music

Recognizing ragas of Carnatic genre using advanced intelligence: a classification system for Indian music

Some New Results on Automatic Identification of Vietnamese Folk Songs Cheo and Quanho

Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification

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