Enabling Access to Sound Archives through Integration, Enrichment and Retrieval Report about polyphonic music transcription

Coordinating meeting, Nice, November Project # What is Automatic Music Transcription Transcription Play/Synthesis

Coordinating meeting, Nice, November Project # Key technologies of polyphonic music transcription  Music onset detection  Polyphonic music transcription

Coordinating meeting, Nice, November Project # A particular time-frequency analysis tool: Resonator Time-frequency Image (RTFI)  Computation-efficient  implemented by the first-order complex resonator filter bank  development of multi-resolution fast implementation  A uniform Framework of TF analysis for music signal  unlike Cohen’s class and Affine class, RTFI is not limited to either constant-band or constant-Q  by simply setting several parameters, the RTFI can implement different TF analysis such as constant-band, constant-Q and ear- like TF analysis  a frequency-dependent time-frequency analysis

Coordinating meeting, Nice, November Project # Music Onset Detection  What is music onset detection  detection of the instant when a new event begins in acoustical signal  hard Onset (fast transition with big energy change)  soft Onset (slow transition with small energy change)  How human detect onset  energy change  pitch change  timbre change

Coordinating meeting, Nice, November Project # Onset detection method in EASAIER  Time-Frequency Processing :  incorporating psychoacoustics knowledge about loudness perception  making energy-change and pitch-change as clear as possible  Detection Algorithms: detecting onsets by both energy and pitch change clues

Coordinating meeting, Nice, November Project # Problems in Polyphonic Pitch Estimation  Harmonic components of different music notes may overlap  In-harmonic: some music instrument have inharmonic timbre

Coordinating meeting, Nice, November Project # Polyphonic Pitch Estimation Method in EASAIER 5 Steps: 1)Performing RTFI analysis 2)Extracting harmonic components 3)Making preliminary estimation of possible pitches 4)Cancelling the extra pitches by checking harmonic components ( simple timbre model) 5)Checking pitch candidates by spectral smoothing principle

Coordinating meeting, Nice, November Project # Compared with other state-of-art methods MIREX 2007 Evaluation  Music onset detection  According to the overall performance, our method wins this contest  Polyphonic pitch estimation (Multiple-F0 estimation task)  our method performed third best in the submitted 16 methods. The performance differences between our method and the first and second best method are minor.  but most computationally efficient, about 10 time faster than the first best method, and 100 time faster than the second best method

Coordinating meeting, Nice, November Project # Future plan  To develop the method for note offset detection  To estimate the note duration time  To improve and evaluate the automation music transcription system  To apply the transcription system assisting the other functionalities such as content-based music retrieval