Investigating key detection to facilitate harmonic mixing

Abstract

Music track mixing is the process that DJs undertake to transit from one track to another, whilst maintaining a continuous play. Next track decisions are generally based on two musical characteristics: the tempo and the key. A track will synchronise with another, if both tracks are at the same tempo. However, even if two tracks are synchronised, the effect they create is not necessarily audibly pleasing. Essentially, two tracks will mix well if they reside in the same or compatible keys. Harmonic Mixing is a solution to this problem. The primary aim of this dissertation is to investigate the different techniques that lead to the extraction of the key from a song in audio file formats. The dissertation is also accompanied by a proof of concept application that aids DJs and musicians in achieving harmonic mixing by automating the processes of key and tempo detection and recommending a choice tracks that are in the same or compatible keys. The key detection algorithm converts the audio signal to the frequency domain using the Short Time Fourier Transform. The frequencies in Hz are mapped to Pitch Class Profile bins, resulting in a 12 dimensional chroma vector. Each dimension represents intensities of each semi-tone class (chroma) along time. Correlation of the chroma vectors with a set of pre-defined binary templates representing the 24 possible keys is then performed, generating a correlation co-efficient for each possible key. Finally, a fair weighting system is used to extract the most probable key. Furthermore, various recommender tools are presented. Next track candidates can either be harmonically compatible or within a user specified tempo range boundary. The application also offers whole mix sequence recommendations corresponding to a set of user specified criteria, including a starting track and its position and the accepted tempo difference between successive tracks. The testing stage ensures that the parameters which provide the best results are used in the final key detection algorithm. This step contributes positively to the final accuracy rate. In fact, in the evaluation, an average accuracy rate of 80% was recorded, when testing the key detection on test sets of classical and dance music. When testing for the correctness using a black-box approach, the recommender functions returned the expected and correct combinations.