Automatic transcription of Turkish makam music

In this paper we propose an automatic system for transcribing/nmakam music of Turkey. We document the specific/ntraits of this music that deviate from properties that/nwere targeted by transcription tools so far and we compile/na dataset of makam recordings along with aligned microtonal/nground-truth. An existing multi-pitch detection algorithm/nis adapted for transcribing music in 20 cent resolution,/nand the final transcription is centered around the/ntonic frequency of the recording. Evaluation metrics for/ntranscribing microtonal music are utilized and results show/nthat transcription of Turkish makam music in e.g. an interactive/ntranscription software is feasible using the current/nstate-of-the-art.This work is partly supported by the European/nResearch Council under the European Union’s Seventh/nFramework Program, as part of the CompMusic project/n(ERC grant agreement 267583)

Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates

Score-informed transcription for automatic piano tutoring

In this paper, a score-informed transcription method for automatic piano tutoring is proposed. The method takes as input a recording made by a student which may contain mistakes, along with a reference score. The recording and the aligned synthesized score are automatically transcribed using the non-negative matrix factorization algorithm for multi-pitch estimation and hidden Markov models for note tracking. By comparing the two transcribed recordings, common errors occurring in transcription algorithms such as extra octave notes can be suppressed. The result is a piano-roll description which shows the mistakes made by the student along with the correctly played notes. Evaluation was performed on six pieces recorded using a Disklavier piano, using both manually-aligned and automatically-aligned scores as an input. Results comparing the system output with ground-truth annotation of the original recording reach a weighted F-measure of 93%, indicating that the proposed method can successfully analyze the student's performance

Incorporating pitch class profiles for improving automatic transcription of Turkish makam music

In this paper we evaluate the impact of including knowledge about scale material into a system for the transcription of Turkish makam music. To this end, we extend our previously presented approach by a refinement iteration that gives preference to note values present in the scale of the mode (i.e. makam). The information about the scalar material is provided in form of pitch class profiles, and they are imposed in form of a Dirichlet prior to our expanded probabilistic latent component analysis (PLCA) transcription system. While the inclusion of such a prior was supposed to focus the transcription system on musically meaningful areas, the obtained results are significantly improved only for recordings of certain instruments. In our discussion we demonstrate the quality of the obtained transcriptions, and discuss the difficulties caused for evaluation in the context of microtonal music

Multiple-instrument polyphonic music transcription using a temporally constrained shift-invariant model

A method for automatic transcription of polyphonic music is proposed in this work that models the temporal evolution of musical tones. The model extends the shift-invariant probabilistic latent component analysis method by supporting the use of spectral templates that correspond to sound states such as attack, sustain, and decay. The order of these templates is controlled using hidden Markov model-based temporal constraints. In addition, the model can exploit multiple templates per pitch and instrument source. The shift-invariant aspect of the model makes it suitable for music signals that exhibit frequency modulations or tuning changes. Pitch-wise hidden Markov models are also utilized in a postprocessing step for note tracking. For training, sound state templates were extracted for various orchestral instruments using isolated note samples. The proposed transcription system was tested on multiple-instrument recordings from various datasets. Experimental results show that the proposed model is superior to a non-temporally constrained model and also outperforms various state-of-the-art transcription systems for the same experiment

Improving automatic music transcription through key detection

In this paper, a method for automatic transcription of polyphonic music is proposed that exploits key information. The proposed system performs key detection using a matching technique with distributions of pitch class pairs, called Zweiklang profiles. The automatic transcription system is based on probabilistic latent component analysis, supporting templates from multiple instruments, as well as tuning deviations and frequency modulations. Key information is incorporated to the transcription system using Dirichlet priors during the parameter update stage. Experiments are performed on a polyphonic, multiple-instrument dataset of Bach chorales, where it is shown that incorporating key information improves multi-pitch detection and instrument assignment performance

On the Memory Properties of Recurrent Neural Models

In this paper, we investigate the memory properties of two popular gated units: long short term memory (LSTM) and gated recurrent units (GRU), which have been used in recurrent neural networks (RNN) to achieve state-of-the-art performance on several machine learning tasks. We propose five basic tasks for isolating and examining specific capabilities relating to the implementation of memory. Results show that (i) both types of gated unit perform less reliably than standard RNN units on tasks testing fixed delay recall, (ii) the reliability of stochastic gradient descent decreases as network complexity increases, and (iii) gated units are found to perform better than standard RNNs on tasks that require values to be stored in memory and updated conditionally upon input to the network. Task performance is found to be surprisingly independent of network depth (number of layers) and connection architecture. Finally, visualisations of the solutions found by these networks are presented and explored, exposing for the first time how logic operations are implemented by individual gated cells and small groups of these cells