Download Neural Sample-Based Piano Synthesis Piano sound emulation has been an active topic of research and development for several decades. Although comprehensive physicsbased piano models have been proposed, sample-based piano emulation is still widely utilized for its computational efficiency and
relative accuracy despite presenting significant memory storage
requirements. This paper proposes a novel hybrid approach to
sample-based piano synthesis aimed at improving the fidelity of
sound emulation while reducing memory requirements for storing samples. A neural network-based model processes the sound
recorded from a single example of piano key at a given velocity.
The network is trained to learn the nonlinear relationship between
the various velocities at which a piano key is pressed and the corresponding sound alterations. Results show that the method achieves
high accuracy using a specific neural architecture that is computationally efficient, presenting few trainable parameters, and it requires memory only for one sample for each piano key.
Download DDSP-SFX: Acoustically-Guided Sound Effects Generation with Differentiable Digital Signal Processing Controlling the variations of sound effects using neural audio synthesis models has been a challenging task. Differentiable digital signal processing (DDSP) provides a lightweight solution that achieves high-quality sound synthesis while enabling deterministic acoustic attribute control by incorporating pre-processed audio features and digital synthesizers. In this research, we introduce DDSP-SFX, a model based on the DDSP architecture capable of synthesizing high-quality sound effects while enabling users to control the timbre variations easily. We integrate a transient modelling algorithm in DDSP that achieves higher objective evaluation scores and subjective ratings over impulsive signals (footsteps, gunshots). We propose a novel method that achieves frame-level timbre variation control while also allowing deterministic attribute control. We further qualitatively show the timbre transfer performance using voice as the guiding sound.
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 Stationary/transient Audio Separation Using Convolutional Autoencoders Extraction of stationary and transient components from audio has many potential applications to audio effects for audio content production. In this paper we explore stationary/transient separation using convolutional autoencoders. We propose two novel unsupervised algorithms for individual and and joint separation. We describe our implementation and show examples. Our results show promise for the use of convolutional autoencoders in the extraction of sparse components from audio spectrograms, particularly using monophonic sounds.
Download Automatic Control of the Dynamic Range Compressor Using a Regression Model and a Reference Sound Practical experience with audio effects as well as knowledge of their parameters and how they change the sound is crucial when controlling digital audio effects. This often presents barriers for musicians and casual users in the application of effects. These users are more accustomed to describing the desired sound verbally or using examples, rather than understanding and configuring low-level signal processing parameters. This paper addresses this issue by providing a novel control method for audio effects. While a significant body of works focus on the use of semantic descriptors and visual interfaces, little attention has been given to an important modality, the use of sound examples to control effects. We use a set of acoustic features to capture important characteristics of sound examples and evaluate different regression models that map these features to effect control parameters. Focusing on dynamic range compression, results show that our approach provides a promising first step in this direction.
Download Universal Audio Synthesizer Control with Normalizing Flows The ubiquity of sound synthesizers have reshaped music production and even entirely define new music genres. However, the increasing complexity and number of parameters in modern synthesizers make them harder to master. Hence, the development of methods allowing to easily create and explore with synthesizers is a crucial need. Here, we introduce a radically novel formulation of audio synthesizer control by formalizing it as finding an organized continuous latent space of audio that represents the capabilities of a synthesizer and map this space to the space of synthesis parameter. By using this formulation, we show that we can address simultaneously automatic parameter inference, macro-control learning and audio-based preset exploration within a single model. To solve this new formulation, we rely on Variational Auto-Encoders (VAE) and Normalizing Flows (NF) to organize and map the respective auditory and parameter spaces. We introduce a new type of NF named regression flows that allow to perform an invertible mapping between separate latent spaces, while steering the organization of some of the latent dimensions. We evaluate our proposal against a large set of baseline models and show its superiority in both parameter inference and audio reconstruction. We also show that the model disentangles the major factors of audio variations as latent dimensions, that can be directly used as macro-parameters. Finally, we discuss the use of our model in several creative applications and introduce real-time implementations in Ableton Live
Download A Simple and Effective Spectral Feature for Speech Detection in Mixed Audio Signals We present a simple and intuitive spectral feature for detecting the presence of spoken speech in mixed (speech, music, arbitrary sounds and noises) audio signals. The feature is based on some simple observations about the appearance, in signals that contain speech, of harmonics with characteristic trajectories. Experiments with some 70 hours of radio broadcasts in five different languages demonstrate that the feature is very effective in detecting and delineating segments that contain speech, and that it also seems to be quite general and robust w.r.t. different languages.
Download Towards Multi-Instrument Drum Transcription Automatic drum transcription, a subtask of the more general automatic music transcription, deals with extracting drum instrument note onsets from an audio source. Recently, progress in transcription performance has been made using non-negative matrix factorization as well as deep learning methods. However, these works primarily focus on transcribing three drum instruments only: snare drum, bass drum, and hi-hat. Yet, for many applications, the ability to transcribe more drum instruments which make up standard drum kits used in western popular music would be desirable. In this work, convolutional and convolutional recurrent neural networks are trained to transcribe a wider range of drum instruments. First, the shortcomings of publicly available datasets in this context are discussed. To overcome these limitations, a larger synthetic dataset is introduced. Then, methods to train models using the new dataset focusing on generalization to real world data are investigated. Finally, the trained models are evaluated on publicly available datasets and results are discussed. The contributions of this work comprise: (i.) a large-scale synthetic dataset for drum transcription, (ii.) first steps towards an automatic drum transcription system that supports a larger range of instruments by evaluating and discussing training setups and the impact of datasets in this context, and (iii.) a publicly available set of trained models for drum transcription. Additional materials are available at http://ifs.tuwien.ac.at/~vogl/dafx2018.
Download A Comparison of Deep Learning Inference Engines for Embedded Real-Time Audio Classification Recent advancements in deep learning have shown great potential for audio applications, improving the accuracy of previous solutions for tasks such as music transcription, beat detection, and real-time audio processing. In addition, the availability of increasingly powerful embedded computers has led many deep learning framework developers to devise software optimized to run pretrained models in resource-constrained contexts. As a result, the use of deep learning on embedded devices and audio plugins has become more widespread. However, confusion has been rising around deep learning inference engines, regarding which of these can run in real-time and which are less resource-hungry. In this paper, we present a comparison of four available deep learning inference engines for real-time audio classification on the CPU of an embedded single-board computer: TensorFlow Lite, TorchScript, ONNX Runtime, and RTNeural. Results show that all inference engines can execute neural network models in real-time with appropriate code practices, but execution time varies between engines and models. Most importantly, we found that most of the less-specialized engines offer great flexibility and can be used effectively for real-time audio classification, with slightly better results than a real-time-specific approach. In contrast, more specialized solutions can offer a lightweight and minimalist alternative where less flexibility is needed.