Download Analysis and Correction of Maps Dataset Automatic music transcription (AMT) is the process of converting the original music signal into the digital music symbol. The MIDI Aligned Piano Sounds (MAPS) dataset was established in 2010 and is the most used benchmark dataset for automatic piano music transcription. In this paper, error screening is carried out through algorithm strategy, and three data annotation problems are found in ENSTDkCl, which is a subset of MAPS, usually used for algorithm evaluation: (1) there are 342 deviation errors of midi annotation; (2) there are 803 unplayed note errors; (3) there are 1613 slow starting process errors. After algorithm correction and manual confirmation, the corrected dataset is released. Finally, the better-performing Google model and our model are evaluated on the corrected dataset. The F values are 85.94% and 85.82%, respectively, and it is correspondingly improved compared with the original dataset, which proves that the correction of the dataset is meaningful.
Download Statistical Sinusoidal Modeling for Expressive Sound Synthesis Statistical sinusoidal modeling represents a method for transferring a sample library of instrument sounds into a data base of sinusoidal parameters for the use in real time additive synthesis. Single sounds, capturing an instrument in combinations of pitch and intensity, are therefor segmented into attack, sustain and release. Partial amplitudes, frequencies and Bark band energies are calculated for all sounds and segments. For the sustain part, all partial and noise parameters are transformed to probabilistic distributions. Interpolated inverse transform sampling is introduced for generating parameter trajectories during synthesis in real time, allowing the creation of sounds located at pitches and intensities between the actual support points of the sample library. Evaluation is performed by qualitative analysis of the system response to sweeps of the control parameters pitch and intensity. Results for a set of violin samples demonstrate the ability of the approach to model dynamic timbre changes, which is crucial for the perceived quality of expressive sound synthesis.
Download A general-purpose deep learning approach to model time-varying audio effects Audio processors whose parameters are modified periodically over time are often referred as time-varying or modulation based audio effects. Most existing methods for modeling these type of effect units are often optimized to a very specific circuit and cannot be efficiently generalized to other time-varying effects. Based on convolutional and recurrent neural networks, we propose a deep learning architecture for generic black-box modeling of audio processors with long-term memory. We explore the capabilities of deep neural networks to learn such long temporal dependencies and we show the network modeling various linear and nonlinear, time-varying and time-invariant audio effects. In order to measure the performance of the model, we propose an objective metric based on the psychoacoustics of modulation frequency perception. We also analyze what the model is actually learning and how the given task is accomplished.
Download Large-scale Real-time Modular Physical Modeling Sound Synthesis Due to recent increases in computational power, physical modeling synthesis is now possible in real time even for relatively complex models. We present here a modular physical modeling instrument design, intended as a construction framework for string- and bar- based instruments, alongside a mechanical network allowing for arbitrary nonlinear interconnection. When multiple nonlinearities are present in a feedback setting, there are two major concerns. One is ensuring numerical stability, which can be approached using an energy-based framework. The other is coping with the computational cost associated with nonlinear solvers—standard iterative methods, such as Newton-Raphson, quickly become a computational bottleneck. Here, such iterative methods are sidestepped using an alternative energy conserving method, allowing for great reduction in computational expense or, alternatively, to real-time performance for very large-scale nonlinear physical modeling synthesis. Simulation and benchmarking results are presented.
Download Time Scale Modification of Audio Using Non-Negative Matrix Factorization This paper introduces an algorithm for time-scale modification of audio signals based on using non-negative matrix factorization. The activation signals attributed to the detected components are used for identifying sound events. The segmentation of these events is used for detecting and preserving transients. In addition, the algorithm introduces the possibility of preserving the envelopes of overlapping sound events while globally modifying the duration of an audio clip.
Download Audio Transport: A Generalized Portamento via Optimal Transport This paper proposes a new method to interpolate between two audio signals. As an interpolation parameter is changed, the pitches in one signal slide to the pitches in the other, producing a portamento, or musical glide. The assignment of pitches in one sound to pitches in the other is accomplished by solving a 1-dimensional optimal transport problem. In addition, we introduce several techniques that preserve the audio fidelity over this highly nonlinear transformation. A portamento is a natural way for a musician to transition between notes, but traditionally it has only been possible for instruments with a continuously variable pitch like the human voice or the violin. Audio transport extends the portamento to any instrument, even polyphonic ones. Moreover, the effect can be used to transition between different instruments, groups of instruments, or any other pair of audio signals. The audio transport effect operates in real-time; we provide an open-source implementation. In experiments with sinusoidal inputs, the interpolating effect is indistinguishable from ideal sine sweeps. More generally, the effect produces clear, musical results for a wide variety of inputs.
Download Optimization of audio graphs by resampling Interactive music systems are dynamic real-time systems which combine control and signal processing based on an audio graph. They are often used on platforms where there are no reliable and precise real-time guarantees. Here, we present a method of optimizing audio graphs and finding a compromise between audio quality and gain in execution time by downsampling parts of the graph. We present models of quality and execution time and we evaluate the models and our optimization algorithm experimentally.
Download Real-Time Modal Synthesis of Crash Cymbals with Nonlinear Approximations, Using a GPU We apply modal synthesis to create a virtual collection of crash cymbals. Synthesizing each cymbal may require enough modes to stress a modern CPU, so a full drum set would certainly not be tractable in real-time. To work around this, we create a GPU-accelerated modal filterbank, with each individual set piece allocated over two thousand modes. This takes only a fraction of available GPU floating-point throughput. With CPU resources freed up, we explore methods to model the different instrument response in the linear/harmonic and non-linear/inharmonic regions that occur as more energy is present in a cymbal: a simple approach, yet one that preserves the parallelism of the problem, uses multisampling, and a more physically-based approach approximates modal coupling.
Download Visualaudio-Design – Towards a Graphical Sounddesign VisualAudio-Design (VAD) is a spectral-node based approach to visually design audio collages and sounds. The spectrogram as a visualization of the frequency-domain can be intuitively manipulated with tools known from image processing. Thereby, a more comprehensible sound design is described to address common abstract interfaces for DSP algorithms that still use direct value inputs, sliders, or knobs. In addition to interaction in the timedomain of audio and conventional analysis and restoration tasks, there are many new possibilities for spectral manipulation of audio material. Here, affine transformations and two-dimensional convolution filters are proposed.