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 Towards Efficient Modelling of String Dynamics: A Comparison of State Space and Koopman Based Deep Learning Methods This paper presents an examination of State Space Models (SSM) and Koopman-based deep learning methods for modelling the dynamics of both linear and non-linear stiff strings. Through experiments with datasets generated under different initial conditions and sample rates, we assess the capacity of these models to accurately model the complex behaviours observed in string dynamics. Our findings indicate that our proposed Koopman-based model performs as well as or better than other existing approaches in nonlinear cases for long-sequence modelling. We inform the design of these architectures with the structure of the problems at hand. Although challenges remain in extending model predictions beyond the training horizon (i.e., extrapolation), the focus of our investigation lies in the models’ ability to generalise across different initial conditions within the training time interval. This research contributes insights into the physical modelling of dynamical systems (in particular those addressing musical acoustics) by offering a comparative overview of these and previous methods and introducing innovative strategies for model improvement. Our results highlight the efficacy of these models in simulating non-linear dynamics and emphasise their wide-ranging applicability in accurately modelling dynamical systems over extended sequences.
Download Fast Differentiable Modal Simulation of Non-Linear Strings, Membranes, and Plates Modal methods for simulating vibrations of strings, membranes, and plates are widely used in acoustics and physically
informed audio synthesis. However, traditional implementations,
particularly for non-linear models like the von Kármán plate, are
computationally demanding and lack differentiability, limiting inverse modelling and real-time applications. We introduce a fast,
differentiable, GPU-accelerated modal framework built with the
JAX library, providing efficient simulations and enabling gradientbased inverse modelling.
Benchmarks show that our approach
significantly outperforms CPU and GPU-based implementations,
particularly for simulations with many modes. Inverse modelling
experiments demonstrate that our approach can recover physical
parameters, including tension, stiffness, and geometry, from both
synthetic and experimental data. Although fitting physical parameters is more sensitive to initialisation compared to methods that
fit abstract spectral parameters, it provides greater interpretability
and more compact parameterisation. The code is released as open
source to support future research and applications in differentiable
physical modelling and sound synthesis.
Download Drumkit Transcription via Convolutive NMF Audio to midi software exists for transcribing the output of a multimic’ed drumkit. Such software requires that the drummer uses multiple microphones to capture a single stream of audio for each kit piece. This paper explores the first steps towards a system for transcribing a drum score based upon the input of a single mono microphone. Non-negative Matrix Factorisation is a widely researched source separation technique. We describe a system for transcribing drums using this technique presenting an improved gains update method. A good level of accuracy is achieved on on complex loops and there are indications the mis-transcriptions are for perceptually less important parts of the score.
Download A Hybrid Approach to Musical Note Onset Detection Common problems with current methods of musical note onset detection are detection of fast passages of musical audio, detection of all onsets within a passage with a strong dynamic range and detection of onsets of varying types, such as multi-instrumental music. We present a method that uses a subband decomposition approach to onset detection. An energy-based detector is used on the upper subbands to detect strong transient events. This yields precision in the time resolution of the onsets, but does not detect softer or weaker onsets. A frequency based distance measure is formulated for use with the lower subbands, improving detection accuracy of softer onsets. We also present a method for improving the detection function, by using a smoothed difference metric. Finally, we show that the detection threshold may be set automatically from analysis of the statistics of the detection function, with results comparable in most places to manual setting of thresholds.