1 Automatic Music Style Recognition Arturo Camacho

2 Contents Goal Definitions Method Results Conclusions Further work

3 Goal Classify songs given as MIDI files into the Latin styles Salsa, Merengue and Cumbia.

4 Definitions MIDI file: file that represents a song as a sequence of musical notes and the time at which they must be played. Audio file: file that represents a sound as a sequence of numbers that correspond to samples of the corresponding wave.

5 Method Method is based on the following observations: – Latin Rhythms have very well defined patterns for most instruments. – These patterns are highly repetitive. They are present most of the time during a song.

6 Method Make templates of the rhythms. Compare songs with templates. Measure the degree of similarity and decide about the membership of the song to the style.

7 Making Templates Listen to many songs with the same style and identify patterns for each of the instruments. Identify similar patterns and group them together into a template. Make variations of a pattern optional elements.

8 Example of Template Salsa common bass patterns: Template for bass rhythm: – Compiled into one measure: “3-2 Clave” “2-3 Clave” () ()

9 Comparing song with Template Compare your song with the template and sum one point for every match. – Example: Your song: Template: If the score is higher than some predetermined threshold accept the song, otherwise reject it. +1 ()() if 13 > threshold  accept if 16

10 Using more than one instrument Make a template for each of the instruments. Compare each of the instruments in your song with the corresponding template. Accept the song if the number of accepted instruments is higher than some predetermined threshold.

11 Using more than one style Make templates for each of the styles. Compare your song with each of them and list all the styles that accepted the song. If more than one style accepted the song, take the style with highest score.

12 Results Method was implemented to identify Salsa, Merengue, and Cumbia based on bass and congas. It was tested with 9, 12, and 12 songs of each style, respectively. 88% of the songs were correctly classified.

13 Conclusions The method described is based on the fact that most of music is highly repetitive. Given this, templates for each style were identified and methods to compare a song with such templates were developed. It showed to be very effective. In the experiments it classified correctly 88% of the songs.

14 Further work: Latin Rhythms Identification on AUDIO files Initial approach: identification based on bass line – Lowest frequency component is usually played by the bass. – Method: Extract the lowest frequency component and try to identify the bass notes. Recognize beats. Apply same methodology as before. Applications: – Search of music by style in multimedia databases, personal collections, and audio devices.