Sine, Transient, Noise Neural Modeling of Piano Notes

This paper introduces a novel method for emulating piano sounds. We propose to exploit the sine, transient, and noise decomposition to design a differentiable spectral modeling synthesizer replicating piano notes. Three sub-modules learn these components from piano recordings and generate the corresponding harmonic, transient, and noise signals. Splitting the emulation into three independently trainable models reduces the modeling tasks' complexity. The quasi-harmonic content is produced using a differentiable sinusoidal model guided by physics-derived formulas, whose parameters are automatically estimated from audio recordings. The noise sub-module uses a learnable time-varying filter, and the transients are generated using a deep convolutional network. From singular notes, we emulate the coupling between different keys in trichords with a convolutional-based network. Results show the model matches the partial distribution of the target while predicting the energy in the higher part of the spectrum presents more challenges. The energy distribution in the spectra of the transient and noise components is accurate overall. While the model is more computationally and memory efficient, perceptual tests reveal limitations in accurately modeling the attack phase of notes. Despite this, it generally achieves perceptual accuracy in emulating single notes and trichords.

Comparative Study of Recurrent Neural Networks for Virtual Analog Audio Effects Modeling

Analog electronic circuits are at the core of an important category of musical devices. The nonlinear features of their electronic components give analog musical devices a distinctive timbre and sound quality, making them highly desirable. Artificial neural networks have rapidly gained popularity for the emulation of analog audio effects circuits, particularly recurrent networks. While neural approaches have been successful in accurately modeling distortion circuits, they require architectural improvements that account for parameter conditioning and low latency response. In this article, we explore the application of recent machine learning advancements for virtual analog modeling. We compare State Space models and Linear Recurrent Units against the more common Long Short Term Memory networks. These have shown promising ability in sequence to sequence modeling tasks, showing a notable improvement in signal history encoding. Our comparative study uses these black box neural modeling techniques with a variety of audio effects. We evaluate the performance and limitations using multiple metrics aiming to assess the models' ability to accurately replicate energy envelopes, frequency contents, and transients in the audio signal. To incorporate control parameters we employ the Feature wise Linear Modulation method. Long Short Term Memory networks exhibit better accuracy in emulating distortions and equalizers, while the State Space model, followed by Long Short Term Memory networks when integrated in an encoder decoder structure, outperforms others in emulating saturation and compression. When considering long time variant characteristics, the State Space model demonstrates the greatest accuracy. The Long Short Term Memory and, in particular, Linear Recurrent Unit networks present more tendency to introduce audio artifacts.

DASEE A Synthetic Database of Domestic Acoustic Scenes and Events in Dementia Patients Environment

Access to informative databases is a crucial part of notable research developments. In the field of domestic audio classification, there have been significant advances in recent years. Although several audio databases exist, these can be limited in terms of the amount of information they provide, such as the exact location of the sound sources, and the associated noise levels. In this work, we detail our approach on generating an unbiased synthetic domestic audio database, consisting of sound scenes and events, emulated in both quiet and noisy environments. Data is carefully curated such that it reflects issues commonly faced in a dementia patients environment, and recreate scenarios that could occur in real-world settings. Similarly, the room impulse response generated is based on a typical one-bedroom apartment at Hebrew SeniorLife Facility. As a result, we present an 11-class database containing excerpts of clean and noisy signals at 5-seconds duration each, uniformly sampled at 16 kHz. Using our baseline model using Continues Wavelet Transform Scalograms and AlexNet, this yielded a weighted F1-score of 86.24 percent.