Abstract
Discriminating song and speech from audio is an exigent problem. This is a step toward self-executing categorization in case of audio signal. Foregoing attempts were mostly involved for discriminating speech with nonspeech but relatively not much works were involved for classifying speech and song from audio signal. Mainly perceptual and frequency depended features were associated with the foregoing attempts. Song, whether it is associated with instrument or not, reveals some type of periodicity, whereas this periodicity is absent in case of normal speech. For accurate study of these periodic nature textural features based on its co-occurrence matrix along with its mean and standard deviation are also considered. Support Vector Machine (SVM), Neural Network (NN), and k-Nearest Neighbor (k-NN) have been brought into play for the purpose of taxonomy of speech from song. Speech and song classification precision obtained in this work has been compared with that of some other previous works done to reveal effectiveness of the advised feature set.
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References
Haralick, R.M., Shapiro, L. G.: Computer and Robot Vision, vol. I (1992)
Gerhard, D.: Pitch-based acoustic feature analysis for the discrimination of speech and monophonic singing. Can. Acoust. 30(3), 152–153 (2002)
Gerhard, D.: Perceptual features for a fuzzy speech-song classification. In: IEEE International Conference on Acoustics Speech and Signal Processing, vol. 4, p. 4160 (2002)
Gerhard, D.: Silence as a cue to rhythm in the analysis of speech and song. Can. Acoust. 31(3), 22–23 (2003)
Bugatti, A., Flammini, A., Migliorati, P.: Audio classification in speech and music: a comparison between a statistical and a neural approach. EURASIP J. Adv. Signal Process. 4 (2002)
Tzanetakis, G.: Song-specific bootstrapping of singing voice structure. In: 2004 IEEE International Conference on Multimedia and Expo, ICME 2004, vol. 3, pp. 2027–2030. IEEE (2004)
Lin, R.S., Chen, L.H.: A new approach for classification of generic audio data. Int. J. Pattern Recognit. Artif. Intell. 19(01), 63–78 (2005)
Umbaugh, S.E.: Computer Imaging: Digital Image Analysis and Processing. CRC Press (2005)
Zhang, Y.G., Zhang, C.S.: Separation of music signals by harmonic structure modeling. In: Advances in Neural Information Processing Systems, pp. 1617–1624 (2006)
Ruinskiy, D., Lavner, Y.: An effective algorithm for automatic detection and exact demarcation of breath sounds in speech and song signals. IEEE Trans. Audio Speech Lang. Process. 15(3), 838–850 (2007)
Lavner, Y., Ruinskiy, D.: A decision-tree-based algorithm for speech/music classification and segmentation. EURASIP J. Audio, Speech Music Process. 2009(1) (2009)
Gallardo-Antolín, A., Montero, J.M.: Histogram equalization-based features for speech, music, and song discrimination. IEEE Signal Process. Lett. 17(7), 659–662 (2010)
Salselas, I., Herrera, P.: Music and speech in early development: automatic analysis and classification of prosodic features from two Portuguese variants. J. Port. Linguist. 10(1) (2011)
Sonnleitner, R., Niedermayer, B., Widmer, G., Schlüter, J.: A simple and effective spectral feature for speech detection in mixed audio signals. In: Proceedings of the 15th International Conference on Digital Audio Effects (2012)
Bhavsar, H., Panchal, M.H.: A review on support vector machine for data classification. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 1(10), 185 (2012)
Velayatipour, M., Mosleh, M.: A review on speech-music discrimination methods. Int. J. Comput. Sci. Netw. Solut. 2(2), 67–78 (2014)
Ramalingam, T., Dhanalakshmi, P.: Speech/music classification using wavelet based feature extraction techniques. J. Comput. Sci. 10(1), 34 (2014)
Walk, M.J., Rupp, A.: Pearson product-moment correlation coefficient. Encycl. res. des. 1023–1027, (2010)
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Ghosal, A., Dutta, S., Banerjee, D. (2020). Classification of Speech and Song Using Co-occurrence-Based Approach. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_11
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DOI: https://doi.org/10.1007/978-981-13-9042-5_11
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