On the impact of key detection performance for identifying classical music styles

We study the automatic identification of Western classical music styles by directly using chroma histograms as classification features. Thereby, we evaluate the benefits of knowing a piece's global key for estimating key-related pitch classes. First, we present four automatic key detection systems. We compare their performance on suitable datasets of classical music and optimize the algorithms' free parameters. Using a second dataset, we evaluate automatic classification into the four style periods Baroque, Classical, Romantic, and Modern. To that end, we calculate global chroma statistics of each audio track. We then split up the tracks according to major and minor keys and circularly shift the chroma histograms with respect to the tonic note. Based on these features, we train two individual classifier models for major and minor keys. We test the efficiency of four chroma extraction algorithms for classification. Furthermore, we evaluate the impact of key detection performance on the classification. Additionally, we compare the key-related chroma features to other chroma-based features. We obtain improved performance when using an efficient key detection method for shifting the chroma histograms.