Download Blackboard system and top-down processing for the transcription of simple polyphonic music A system is proposed to perform the automatic music transcription of simple polyphonic tracks using top-down processing. It is composed of a blackboard system of three hierarchical levels, receiving its input from a segmentation routine in the form of an averaged STFT matrix. The blackboard contains a hypotheses database, a scheduler and knowledge sources, one of which is a neural network chord recogniser with the ability to reconfigure the operation of the system, allowing it to output more than one note hypothesis at a time. The basic implementation is explained, and some examples are provided to illustrate the performance of the system. The weaknesses of the current implementation are shown and next steps for further development of the system are defined.
Download Increasing Drum Transcription Vocabulary Using Data Synthesis Current datasets for automatic drum transcription (ADT) are small and limited due to the tedious task of annotating onset events. While some of these datasets contain large vocabularies of percussive instrument classes (e.g. ~20 classes), many of these classes occur very infrequently in the data. This paucity of data makes it difficult to train models that support such large vocabularies. Therefore, data-driven drum transcription models often focus on a small number of percussive instrument classes (e.g. 3 classes). In this paper, we propose to support large-vocabulary drum transcription by generating a large synthetic dataset (210,000 eight second examples) of audio examples for which we have groundtruth transcriptions. Using this synthetic dataset along with existing drum transcription datasets, we train convolutional-recurrent neural networks (CRNNs) in a multi-task framework to support large-vocabulary ADT. We find that training on both the synthetic and real music drum transcription datasets together improves performance on not only large-vocabulary ADT, but also beat / downbeat detection small-vocabulary ADT.
Download Generating Musical Accompaniment Using Finite State Transducers The finite state transducer (FST), a type of finite state machine that maps an input string to an output string, is a common tool in the fields of natural language processing and speech recognition. FSTs have also been applied to music-related tasks such as audio fingerprinting and the generation of musical accompaniment. In this paper, we describe a system that uses an FST to generate harmonic accompaniment to a melody. We provide details of the methods employed to quantize a music signal, the topology of the transducer, and discuss our approach to evaluating the system. We argue for an evaluation metric that takes into account the quality of the generated accompaniment, rather than one that returns a binary value indicating the correctness or incorrectness of the accompaniment.