Feature extraction of musical content for automatic music transcription

The purpose of this thesis is to develop new methods for automatic transcription of melody and harmonic parts of real-life music signal. Music transcription is here defined as an act of analyzing a piece of music signal and writing down the parameter representations, which indicate the pitch, onset time and duration of each pitch, loudness and instrument applied in the analyzed music signal. The proposed algorithms and methods aim at resolving two key sub-problems in automatic music transcription: music onset detection and polyphonic pitch estimation. There are three original contributions in this thesis. The first is an original frequency-dependent time-frequency analysis tool called the Resonator Time-Frequency Image (RTFI). By simply defining a parameterized function mapping frequency to the exponent decay factor of the complex resonator filter bank, the RTFI can easily and flexibly implement the time-frequency analysis with different time-frequency resolutions such as ear-like (similar to human ear frequency analyzer), constant-Q or uniform (evenly-spaced) time-frequency resolutions. The corresponding multi-resolution fast implementation of RTFI has also been developed. The second original contribution consists of two new music onset detection algorithms: Energy-based detection algorithm and Pitch-based detection algorithm. The Energy-based detection algorithm performs well on the detection of hard onsets. The Pitch-based detection algorithm is the first one, which successfully exploits the pitch change clue for the onset detection in real polyphonic music, and achieves a much better performance than the other existing detection algorithms for the detection of soft onsets. The third contribution is the development of two new polyphonic pitch estimation methods. They are based on the RTFI analysis. The first proposed estimation method mainly makes best of the harmonic relation and spectral smoothing principle, consequently achieves an excellent performance on the real polyphonic music signals. The second proposed polyphonic pitch estimation method is based on the combination of signal processing and machine learning. The basic idea behind this method is to transform the polyphonic pitch estimation as a pattern recognition problem. The proposed estimation method is mainly composed by a signal processing block followed by a learning machine. Multi-resolution fast RTFI analysis is used as a signal processing component, and support vector machine (SVM) is selected as learning machine. The experimental result of the first approach show clear improvement versus the other state of the art methods.