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Found 14 papers, 2 papers with code
Interpreting End-to-End Deep Learning Models for Speech Source Localization Using Layer-wise Relevance Propagation
Deep learning models are widely applied in the signal processing community, yet their inner working procedure is often treated as a black box.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Physics-Informed Neural Network for Volumetric Sound field Reconstruction of Speech Signals
Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based approaches, particularly in sound field reconstruction.
HOMULA-RIR: A Room Impulse Response Dataset for Teleconferencing and Spatial Audio Applications Acquired Through Higher-Order Microphones and Uniform Linear Microphone Arrays
In this paper, we present HOMULA-RIR, a dataset of room impulse responses (RIRs) acquired using both higher-order microphones (HOMs) and a uniform linear array (ULA), in order to model a remote attendance teleconferencing scenario.
Room Transfer Function Reconstruction Using Complex-valued Neural Networks and Irregularly Distributed Microphones
Reconstructing the room transfer functions needed to calculate the complex sound field in a room has several impor- tant real-world applications.
Toward Deep Drum Source Separation
In the past, the field of drum source separation faced significant challenges due to limited data availability, hindering the adoption of cutting-edge deep learning methods that have found success in other related audio applications.
Reconstruction of Sound Field through Diffusion Models
Reconstructing the sound field in a room is an important task for several applications, such as sound control and augmented (AR) or virtual reality (VR).
Timbre transfer using image-to-image denoising diffusion implicit models
Timbre transfer techniques aim at converting the sound of a musical piece generated by one instrument into the same one as if it was played by another instrument, while maintaining as much as possible the content in terms of musical characteristics such as melody and dynamics.
Implicit neural representation with physics-informed neural networks for the reconstruction of the early part of room impulse responses
Recently deep learning and machine learning approaches have been widely employed for various applications in acoustics.
Acoustic source localization in the spherical harmonics domain exploiting low-rank approximations
Acoustic signal processing in the spherical harmonics domain (SHD) is an active research area that exploits the signals acquired by higher order microphone arrays.
Synthesis of Soundfields through Irregular Loudspeaker Arrays Based on Convolutional Neural Networks
Most soundfield synthesis approaches deal with extensive and regular loudspeaker arrays, which are often not suitable for home audio systems, due to physical space constraints.
Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network
Near-field Acoustic Holography (NAH) is a well-known problem aimed at estimating the vibrational velocity field of a structure by means of acoustic measurements.
Parametric Optimization of Violin Top Plates using Machine Learning
It allows us to both compute the vibrational behavior of an instrument from its geometry and optimize its shape for a given response.
A Data-Driven Approach to Violin Making
Of all the characteristics of a violin, those that concern its shape are probably the most important ones, as the violin maker has complete control over them.
Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching
The performance of machine learning algorithms is known to be negatively affected by possible mismatches between training (source) and test (target) data distributions.
