A music key detection method based on pitch class distribution theory

As one of the most well-known music elements, music key, which reveals an important feature in music transcription, structure analysis and mood comprehension, is always an essential theoretical construct of music. As a result, key finding is becoming a popular topic of Music Information Retrieval (MIR). In this paper, we propose a novel approach with good robustness to detect keys in polyphonic music from a view of pitch class distribution theory. A signal transformation with musical representation-Constant Q transform (CQT) is firstly applied to music audio for spectrum analysis. Then onset detection and pitch tuning are introduced in order to ensure robustness. Finally, a weighted harmonic structure pattern-pitch class distribution matrix (PCDM) is extracted as feature for key classification. PCDM contains both pitch class information and chord structure, and it is based on pitch class distribution view. Considering the classifier, a neural network is applied to model the pitch class distribution and complete the task of key recognition. Also, a key smoothing method makes proposed method capable of processing modulation and reducing key fluctuation. Experiments showed that the proposed strategy can reach a good performance in polyphonic music at a relatively lower computational cost, and proved our strategy to be quite promising.