Open AccessStudy on a CNN-HMM Approach for Audio-Based Musical Chord Recognition
Author(s) -
Taixiang Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1802/3/032033
Subject(s) - hidden markov model , computer science , artificial intelligence , convolutional neural network , pattern recognition (psychology) , speech recognition , mixture model , chord (peer to peer) , artificial neural network , distributed computing
Convolutional Neural Network (CNN) has shown its strength in image processing task, and Hidden Markov model (HMM) is a powerful tool for modeling sequential data. This paper presents a new architecture for audio-based chord recognition using a CNN-HMM mixture model. This architecture replaces the Gaussian mixture model (GMM) and Deep Neural Network (DNN) layers of GMM-HMM and DNN-HMM models with CNN. The model performance is evaluated through a dataset using different combinations of chroma vectors (STFT, CQT, CENS) as features, based on that, a scale recognition sub-model is tested.