Name
Abstract | ||
|---|---|---|
Being one of the most well-known high-level music content concepts, music key reveals an important theoretical feature for Western music in music structure analysis, music note and chord transcription and music mood comprehension. Key finding becomes a main task of music information retrieval (MIR). In this paper, we propose a novel approach with good robustness to detect keys in polyphonic music based on Artificial Neural Network (ANN). Constant Q transform (CQT) is firstly applied to music signal for CQT spectrum analysis. Then onset detection and pitch tuning are introduced in order to ensure a high robustness. Finally a distribution matrix is generated as music key feature. Considering the classifier, a neural network is applied to model the pitch class distribution and complete the task of key recognition. 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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICDSP.2009.5201119 | Santorini-Hellas |
Keywords | DocType | ISBN |
key detection,music signal,mir,well-known high-level music content,music structure analysis,music key feature,artificial intelligence,pitch class distribution model,music information retrieval,onset detection,music,information retrieval,chord transcription,high-level music content concepts,music mood comprehension,pitch class distribution matrix,distribution matrix,music note,western music,ann,constant q transform,key finding,artificial neural network,polyphonic music,music key,pitch tuning,neural nets,computer science,frequency,tuning,robustness,spectrogram,feature extraction,structure analysis,multiple signal classification,neural network,sun,modulation,signal analysis,artificial neural networks,spectrum analysis | Conference | 978-1-4244-3298-1 |
Citations | PageRank | References |
3 | 0.44 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiayin Sun | 1 | 5 | 1.83 |
| Haifeng Li | 2 | 26 | 10.38 |
| Lei Li | 3 | 187 | 33.91 |