Head-related transfer functions (HRTFs) with dense spatial grids are desired for immersive binaural audio generation, but their recording is time-consuming. Although HRTF spatial upsampling has shown remarkable progress with neural fields, spatial upsampling only from a few measured directions, e.g., 3 or 5 measurements, is still challenging. To tackle this problem, we propose a retrieval-augmented neural field (RANF). RANF retrieves a subject whose HRTFs are close to those of the target subject from a dataset. The HRTF of the retrieved subject at the desired direction is fed into the neural field in addition to the sound source direction itself. Furthermore, we present a neural network that can efficiently handle multiple retrieved subjects, inspired by a multi-channel processing technique called transform-average-concatenate. Our experiments confirm the benefits of RANF on the SONICOM dataset, and it is a key component in the winning solution of Task 2 of the listener acoustic personalization challenge 2024.

Sound event localization and detection (SELD) is an important task in machine listening. Major advancements rely on simulated data with sound events in specific rooms and strong spatio-temporal labels. SELD data is simulated by convolving spatialy-localized room impulse responses (RIRs) with sound waveforms to place sound events in a soundscape. However, RIRs require manual collection in specific rooms. We present SpatialScaper, a library for SELD data simulation and augmentation. Compared to existing tools, SpatialScaper emulates virtual rooms via parameters such as size and wall absorption. This allows for parameterized placement (including movement) of foreground and background sound sources. SpatialScaper also includes data augmentation pipelines that can be applied to existing SELD data. As a case study, we use SpatialScaper to add rooms to the DCASE SELD data. Training a model with our data led to progressive performance improves as a direct function of acoustic diversity. These results show that SpatialScaper is valuable to train robust SELD models.

Understanding the behavioral and neural dynamics of social interactions is a goalof contemporary neuroscience. Many machine learning methods have emergedin recent years to make sense of complex video and neurophysiological data thatresult from these experiments. Less focus has been placed on understanding howanimals process acoustic information, including social vocalizations. A criticalstep to bridge this gap is determining the senders and receivers of acoustic infor-mation in social interactions. While sound source localization (SSL) is a classicproblem in signal processing, existing approaches are limited in their ability tolocalize animal-generated sounds in standard laboratory environments. Advancesin deep learning methods for SSL are likely to help address these limitations,however there are currently no publicly available models, datasets, or benchmarksto systematically evaluate SSL algorithms in the domain of bioacoustics. Here,we present the VCL Benchmark: the first large-scale dataset for benchmarkingSSL algorithms in rodents. We acquired synchronized video and multi-channelaudio recordings of 767,295 sounds with annotated ground truth sources across 9conditions. The dataset provides benchmarks which evaluate SSL performance onreal data, simulated acoustic data, and a mixture of real and simulated data. Weintend for this benchmark to facilitate knowledge transfer between the neuroscienceand acoustic machine learning communities, which have had limited overlap.

Modeling room acoustics in a real-world settings involves some degree of blind parameter estimation from noisy and reverberant audio. Modern approaches leverage convolutional neural networks (CNNs) in tandem with time-frequency representations. Using short-time Fourier transforms to develop these spectrogram-like features has shown promising results, but this method implicitly discards a significant amount of audio information in the phase domain. Inspired by recent works in speech enhancement, we propose utilizing phase-related features to extend recent approaches to blindly estimate the so-called "reverberation fingerprint" parameters, namely, volume and RT60. The addition of these features is shown to outperform existing methods that rely solely on magnitude-based spectral features across a wide range of acoustics spaces. We evaluate the effectiveness of the deployment of these phase features in both single-parameter and multi-parameter estimation strategies, using a task-specific dataset that consists of publicly available room impulse responses (RIRs), synthesized RIRs, and in-house measurements of real acoustic spaces.

Design and execution of high fidelity acoustic sensing in complex environments poses a number of practical challenges, from accurately measuring the geometry of the setup and estimating the channel response, to time synchronization amongst the sources and receivers. When acoustic experiments are performed on-board the International Space Stations (ISS), the number of constraints and obstacles vastly increases, due to the combination of a highly unpredictable acoustic environment, and restricted availability of crew time. In this paper, we present our preliminary results with a first-of-a-kind acoustic imaging experiment performed aboard the ISS, highlighting the difference between simulations, laboratory measurements, and in-space experiments. We hope that these experiments and results will help the research community in realizing high performance acoustic imaging capabilities in complex environments.

Recent literature has demonstrated that the use of per-channel energy normalization (PCEN), has significant performance improvements over traditional log-scaled mel-frequency spectrograms in acoustic sound event detection (SED) in a multi-class setting with overlapping events. However, the configuration of PCEN’s parameters is sensitive to the recording environment, the characteristics of the class of events of interest, and the presence of multiple overlapping events [1]. This leads to improvements on a class-by-class basis, but poor cross-class performance. In this article, we experiment using PCEN spectrograms as an alternative method for SED in urban audio using the UrbanSED dataset, demonstrating per-class improvements based on parameter configuration. Furthermore, we address cross-class performance with PCEN using a novel method, Multi-Rate PCEN (MR-PCEN). We demonstrate cross-class SED performance with MRPCEN, demonstrating improvements to cross-class performance compared to traditional single-rate PCEN.