This paper proposes a new method for local key and chord estimation from audio signals. A harmonic content of the musical piece is first extracted by computing a set of chroma vectors. Correlation with fixed chord and key templates then selects a set of key/chord pairs for every frame. A weighted acyclic harmonic graph is then built with these pairs as vertices, and the use of a musical distance to weigh its edges. Finally, the output sequences of chords and keys are obtained by finding the best path in the graph. The proposed system allows a mutual and beneficial chord and key estimation. It is evaluated on a corpus com-posed of Beatles songs for both the local key estimation and chord recognition tasks. Results show that it performs bette...
In this paper, we propose a system for the automatic estimation of the key of a music track using hi...
In this paper, we discuss the introduction of a trigram musicological model in a simultaneous chord ...
In this paper, we propose a novel method for obtaining labeled training data to estimate the paramet...
This paper proposes a new method for local key and chord estimation from audio signals. This method ...
In this paper, we present a probabilistic framework for the simultaneous estimation of chords and ke...
In this thesis we address the subject of automatic extraction of harmony information from audio reco...
We present a new system for the harmonic analysis of popular musical audio. It is focused on chord e...
In this paper, significant improvements of a previously developed key and chord extraction system ar...
The realisation and evaluation of a musical key extraction algorithm that works directly on raw audi...
This thesis is concerned with the automatic transcription of chords from audio, with an emphasis on ...
Modern collections of symbolic and audio music content provide unprecedented possibilities for music...
In this paper we propose a method of audio chord estimation. It does not rely on any machine learnin...
We present a new system for chord transcription from polyphonic musical audio that uses domain-speci...
Chords and keys are among the most exhaustive descriptors of songs. In this study we focus on chord ...
This thesis aims to develop a style specific approach to Automatic Chord Estimation and computer-aid...
In this paper, we propose a system for the automatic estimation of the key of a music track using hi...
In this paper, we discuss the introduction of a trigram musicological model in a simultaneous chord ...
In this paper, we propose a novel method for obtaining labeled training data to estimate the paramet...
This paper proposes a new method for local key and chord estimation from audio signals. This method ...
In this paper, we present a probabilistic framework for the simultaneous estimation of chords and ke...
In this thesis we address the subject of automatic extraction of harmony information from audio reco...
We present a new system for the harmonic analysis of popular musical audio. It is focused on chord e...
In this paper, significant improvements of a previously developed key and chord extraction system ar...
The realisation and evaluation of a musical key extraction algorithm that works directly on raw audi...
This thesis is concerned with the automatic transcription of chords from audio, with an emphasis on ...
Modern collections of symbolic and audio music content provide unprecedented possibilities for music...
In this paper we propose a method of audio chord estimation. It does not rely on any machine learnin...
We present a new system for chord transcription from polyphonic musical audio that uses domain-speci...
Chords and keys are among the most exhaustive descriptors of songs. In this study we focus on chord ...
This thesis aims to develop a style specific approach to Automatic Chord Estimation and computer-aid...
In this paper, we propose a system for the automatic estimation of the key of a music track using hi...
In this paper, we discuss the introduction of a trigram musicological model in a simultaneous chord ...
In this paper, we propose a novel method for obtaining labeled training data to estimate the paramet...