An effective analysis of deep learning based approaches for audio based feature extraction and its visualization

Author:

Dhiraj ORCID,Biswas Rohit,Ghattamaraju Nischay

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference40 articles.

1. Annesi P, Basili R, Gitto R, Moschitti A, Petitti R (2007) Audio feature engineering for automatic music genre classification. In Large Scale Semantic Access to Content (Text, Image, Video, and Sound), pp. 702-711. LE CENTRE DE HAUTES ETUDES INTERNATIONALES D'INFORMATIQUE DOCUMENTAIRE

2. Baniya BK, Lee J, Li ZN (2014) Audio feature reduction and analysis for automatic music genre classification. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), San Diego, pp. 457–462

3. Benzi K, Defferrard M, Vandergheynst P, Bresson X (2016) Fma: A dataset for music analysis,” arXiv preprint arXiv:1612.01840

4. Chung Y, Wu C, Shen C, Lee H, Lee L (2016) Audio Word2Vec: Unsupervised learning of audio segment representations using sequence-to-sequence autoencoder. Proc. Interspeech, pp. 410–415

5. Ciuha P, Klemenc B, Solina F (2010) Visualization of concurrent tones in music with colours. Univ. of Ljubljana, Slovenia

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