KaydedilenlerDerginizi ekleyin

Dergiler / Istanbul University Journal of Electrical and Electronics Engineering / 2011 / Cilt: 11 - Sayı: 1

Comparison of wavelet based feature extraction methods for speech/music discrimination

Nalan ÖZKURT
Timur DÜZENLİ
Sayfa
1355–1362
Yayın Tarihi
2022-10-18
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

According to the results, the proposed feature set outperforms the traditional ones.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The speech/music discrimination systems have gaining importance in several intelligent audio retrieval algorithms due to the increasing size of the multimedia sources in our daily lives.
📊
Temel Bulgular & Veri:Also, the performance of the discrete wavelet transform and the dual- tree wavelet transform has been compared with the conventional time, frequency and cepstral domain features used in speech/music discrimination.
💡
Sonuç & Katkı:According to the results, the proposed feature set outperforms the traditional ones.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

The speech/music discrimination systems have gaining importance in several intelligent audio retrieval algorithms due to the increasing size of the multimedia sources in our daily lives. This study aims to propose a speech/music discrimination system which utilizes the advantages of the wavelet transform. Also, the performance of the discrete wavelet transform and the dual- tree wavelet transform has been compared with the conventional time, frequency and cepstral domain features used in speech/music discrimination. The speech and music samples collected from common databases, CD recording and internet radios have been classified with artificial neural networks with different feature sets. The principal component analysis has been applied to eliminate the correlated features before classification stage. Considering the number of vanishing moments and orthogonality, the best performance has been obtained with Daubechies8 wavelet among the other members of the Daubechies family. According to the results, the proposed feature set outperforms the traditional ones.

🔖

Bu Makaleye Atıf Yap

ÖZKURT, N., DÜZENLİ, T. (2022). Comparison of wavelet based feature extraction methods for speech/music discrimination. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 11 - Sayı: 1, 1355–1362. https://ajindex.com/articles/543693

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (12)

  1. Ambikairajah, O. M. E., Epps, J., “Novel features for effective speech and music discrimination,” in Proc. IEEE Int. Conf. on Engineering of Intelligent Systems, pp. 1–5, 2006
  2. Exposito, N. R. J.E.M., Galan, S.G., Candeas, P., “Audio coding improvement using evolutionary speech/music discrimination,” in Proc. IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), pp. 1–6, 2007
  3. El-Maleh, K., Petrucci, M. G., Kabal, P., “Speech/music discrimination for multimedia applications,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, pp. 2445–2448, 2000
  4. Gedik, A., Bozkurt, B., “Pitch frequency histogram based music information retrieval for turkish music,” Signal Processing, vol. 10, pp. 1049–1063, 2010
  5. Saunders, J., “Real time discrimination of broadcast speech/music,” in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, pp. 993–996, 1996
  6. Scheier, E., Slaney, M., “Construction and evaluation of a robust multifeature speech/music discriminator,” in Proc. IEEE Int. Conf. On Acoustics, Speech, and Signal Processing, ICASSP’97, pp. 1331–1334, 1997 [7] Ajmera, I. M. J., Bourlard, H., “Speech/music segmentation using entropy and dynamism features in a HMM classification framework,” Speech Communication, vol. 40, pp. 351–363, 2003
  7. Panagiotakis, C., Tziritas, G., “A speech/music discriminator based on RMS and zero-crossings,” IEEE Trans. Multimedia, vol. 7, pp. 155–166, 2005
  8. Tzanetakis, G. E. G., Cook, P., “Audio analysis using the discrete wavelet transform,” in Proc. Conf. in Acoustics and Music Theory Applications. WSES, pp. 318–323, 2001
  9. Didiot E., Illina, I., Fohr, D., Mella, O., “A wavelet- based parameterization for speech/music discrimination,” Computer Speech and Language, vol. 24, pp. 341–357, 2010
  10. Ntalampiras, S., Fakotakis, N., “Speech /music discrimination based on discrete wavelet transform,” in Proc. of 5th Hell. Conf. On Art.Int., SETN’08, LNAI 5138, Greece, Oct. 2008, pp. 205–211, 2008 [12] Khan, M., Al-Khatib, W., “Machine-learning based classification of speech and music,” ACM Jour. on Multimedia Systems, vol. 12, pp. 55–67, 2006
  11. Mallat, S., A wavelet tour of signal processing. Academic Press, 1999 [14] Zheng, F., Zhang, G., Song, Z., “Comparison of different implemantations of mfcc,” Arch. Rat. Mech. Anal., vol. 16, pp. 582–589, 2001
  12. Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.G. “The Dual-Tree ComplexWavelet Transform”, IEEE Sig.Proc. Mag. 22, pp. 123–151, 2005