2005:Audio Key Finding

Proposer

Arpi Mardirossian, Ching-Hua Chuan and Elaine Chew (University of Southern California) mardiros@usc.edu

Title

Evaluation of Key Finding Algorithms

Description

Determination of the key is a prerequisite for any analysis of tonal music. As a result, extensive work has been done in the area of automatic key detection. However, among this plethora of key finding algorithms, what seems to be lacking is a formal and extensive evaluation process. We propose the evaluation of key-finding algorithms at the 2005 MIREX.

There are significant contributions in the area of key finding for both audio and symbolic representation. Thus another the same contest was also proposed for MIDI data. Algorithms that determine the key from audio should be robust enough to handle frequency interferences and harmonic effects caused by the use of multiple instruments.

Potential Participants

1. Emilia G├│mez (egomez@iua.upf.es) and Perfecto Herrera (perfecto.herrera@iua.upf.es): [high].
2. Steffen Pauws (steffen.pauws@philips.com): [high].
3. Ching-Hua Chuan (chinghuc@usc.edu) and Elaine Chew (echew@usc.edu): [high].
4. Ozgur Izmirli (oizm@conncoll.edu): [moderate].
5. Yongwei Zhu (ywzhu@i2r.a-start.edu.sg) and Mohan Kankanhalli (mohan@comp.nus.edu.sg): [unknown].

Evaluation Procedures

The following evaluation outline is a general guideline that will be compatible with both audio and symbolic key finding algorithms. It is safe to assume that each key finding algorithm will have its own set of parameters. The creators of the system should pre-determine the optimal settings for the parameters. Once these settings are determined, an accuracy rate may be calculated. The input of the test should be some excerpt of the pieces in the test set and the output will be the key name, for example, C major or E flat minor. We plan to use pieces for which the keys are known, for example, symphonies and concertos by well-known composers where the keys are stated in the title of the piece. The excerpt will typically be the beginnings of the pieces as this is the only part of the piece for which establishing of the global and known key can be guaranteed.

The error analysis will center on comparing the key identified by the algorithm to the actual key of the piece. We will then determine how 'close' each identified key is to the corresponding correct key. Keys will be considered as 'close' if they have one of the following relationships: distance of perfect fifth, relative major and minor, and parallel major and minor. It can be assumed that if an algorithm returns a key that is closely related to the actual key then it is superior. We may then use this information to generate further metrics.

Clearly, the optimal parameters may vary for different styles of music, and by composer. If time permits and the systems allow, we may next focus on pieces for which the algorithm has identified an incorrect key under the optimal settings of the parameters and determine whether the incorrect assignments were due to improper parameter selection. We may then calculate the percent of the pieces that had an incorrect assignment under the optimal settings but have a correct assignment with other settings.

Relevant Test Collections

Audio data can be obtained from HNH Hong Kong International, Ltd. (http://www.naxos.com), if the agreement with the company is now in effect for MIR testing. We have determined that only fifteen to thirty second excerpts may be sufficient for key finding using audio data. Copyright regulations state that up to 33% of audio files may be copied without any violations of such regulations. This is advantageous since fifteen to thirty second excerpts will be well within this limit.