Automatic Chord Recognition with Higher-Order Harmonic Language Modelling

08/16/2018
by   Filip Korzeniowski, et al.
0

Common temporal models for automatic chord recognition model chord changes on a frame-wise basis. Due to this fact, they are unable to capture musical knowledge about chord progressions. In this paper, we propose a temporal model that enables explicit modelling of chord changes and durations. We then apply N-gram models and a neural-network-based acoustic model within this framework, and evaluate the effect of model overconfidence. Our results show that model overconfidence plays only a minor role (but target smoothing still improves the acoustic model), and that stronger chord language models do improve recognition results, however their effects are small compared to other domains.

READ FULL TEXT
research
08/16/2018

Improved Chord Recognition by Combining Duration and Harmonic Language Models

Chord recognition systems typically comprise an acoustic model that pred...
research
08/07/2015

An End-to-End Neural Network for Polyphonic Piano Music Transcription

We present a supervised neural network model for polyphonic piano music ...
research
06/21/2021

Computational Pronunciation Analysis in Sung Utterances

Recent automatic lyrics transcription (ALT) approaches focus on building...
research
12/03/2020

End to End ASR System with Automatic Punctuation Insertion

Recent Automatic Speech Recognition systems have been moving towards end...
research
05/17/2017

Frame Stacking and Retaining for Recurrent Neural Network Acoustic Model

Frame stacking is broadly applied in end-to-end neural network training ...
research
12/02/2021

A higher order Minkowski loss for improved prediction ability of acoustic model in ASR

Conventional automatic speech recognition (ASR) system uses second-order...
research
11/19/2019

Multimedia Search and Temporal Reasoning

Properly modelling dynamic information that changes over time still is a...

Please sign up or login with your details

Forgot password? Click here to reset