Blind Localization of Room Reflections with Application to Spatial Audio

Blind estimation of early room reflections, without knowledge of the room impulse response, holds substantial value. The FF-PHALCOR (Frequency Focusing PHase ALigned CORrelation), method was recently developed for this objective, extending the original PHALCOR method from spherical to arbitrary arrays. However, previous studies only compared the two methods under limited conditions without presenting a comprehensive performance analysis. This study presents an advance by evaluating the performance of the algorithm in a wider range of conditions. Additionally, performance in terms of perception is investigated through a listening test. This test involves synthesizing room impulse responses from known room acoustics parameters and replacing the early reflections with the estimated ones. The importance of the estimated reflections for spatial perception is demonstrated through this test.

Chat2Map: Efficient Scene Mapping from Multi-Ego Conversations

Can conversational videos captured from multiple egocentric viewpoints reveal the map of a scene in a cost-efficient way? We seek to answer this question by proposing a new problem: efficiently building the map of a previously unseen 3D environment by exploiting shared information in the egocentric audio-visual observations of participants in a natural conversation. Our hypothesis is that as multiple people ("egos") move in a scene and talk among themselves, they receive rich audio-visual cues that can help uncover the unseen areas of the scene. Given the high cost of continuously processing egocentric visual streams, we further explore how to actively coordinate the sampling of visual information, so as to minimize redundancy and reduce power use. To that end, we present an audio-visual deep reinforcement learning approach that works with our shared scene mapper to selectively turn on the camera to efficiently chart out the space. We evaluate the approach using a state-of-the-art audio-visual simulator for 3D scenes as well as real-world video. Our model outperforms previous state-of-the-art mapping methods, and achieves an excellent cost-accuracy tradeoff. Project: http://vision.cs.utexas.edu/projects/chat2map.

Towards Improved Room Impulse Response Estimation for Speech Recognition

We propose to characterize and improve the performance of blind room impulse response (RIR) estimation systems in the context of a downstream application scenario, far-field automatic speech recognition (ASR). We first draw the connection between improved RIR estimation and improved ASR performance, as a means of evaluating neural RIR estimators. We then propose a GAN-based architecture that encodes RIR features from reverberant speech and constructs an RIR from the encoded features, and uses a novel energy decay relief loss to optimize for capturing energy-based properties of the input reverberant speech. We show that our model outperforms the state-of-the-art baselines on acoustic benchmarks (by 72% on the energy decay relief and 22% on an early-reflection energy metric), as well as in an ASR evaluation task (by 6.9% in word error rate).

Direct and Residual Subspace Decomposition of Spatial Room Impulse Responses

Psychoacoustic experiments have shown that directional properties of, in particular, the direct sound, salient reflections, and the late reverberation of an acoustic room response can have a distinct influence on the auditory perception of a given room. Spatial room impulse responses (SRIRs) capture those properties and thus are used for direction-dependent room acoustic analysis and virtual acoustic rendering. This work proposes a subspace method that decomposes SRIRs into a direct part, which comprises the direct sound and the salient reflections, and a residual, to facilitate enhanced analysis and rendering methods by providing individual access to these components. The proposed method is based on the generalized singular value decomposition and interprets the residual as noise that is to be separated from the other components of the reverberation. It utilizes a noise estimate to identify large generalized singular values, which are then attributed to the direct part. By advancing from the end of the SRIR toward the beginning while iteratively updating the noise estimate, the method is able to work with anisotropic and slowly time-varying reverberant sound fields. The proposed method does not require direction-of-arrival estimation of reflections and shows an improved separation of the direct part from the residual compared to an existing approach. A case study with measured SRIRs suggests a high robustness of the method under different acoustic conditions. A reference implementation is provided.

SAQAM: Spatial Audio Quality Assessment Metric

Audio quality assessment is critical for assessing the perceptual realism of sounds. However, the time and expense of obtaining ''gold standard'' human judgments limit the availability of such data. For AR&VR, good perceived sound quality and localizability of sources are among the key elements to ensure complete immersion of the user. Our work introduces SAQAM which uses a multi-task learning framework to assess listening quality (LQ) and spatialization quality (SQ) between any given pair of binaural signals without using any subjective data. We model LQ by training on a simulated dataset of triplet human judgments, and SQ by utilizing activation-level distances from networks trained for direction of arrival (DOA) estimation. We show that SAQAM correlates well with human responses across four diverse datasets. Since it is a deep network, the metric is differentiable, making it suitable as a loss function for other tasks. For example, simply replacing an existing loss with our metric yields improvement in a speech-enhancement network.

SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning

We introduce SoundSpaces 2.0, a platform for on-the-fly geometry-based audio rendering for 3D environments. Given a 3D mesh of a real-world environment, SoundSpaces can generate highly realistic acoustics for arbitrary sounds captured from arbitrary microphone locations. Together with existing 3D visual assets, it supports an array of audio-visual research tasks, such as audio-visual navigation, mapping, source localization and separation, and acoustic matching. Compared to existing resources, SoundSpaces 2.0 has the advantages of allowing continuous spatial sampling, generalization to novel environments, and configurable microphone and material properties. To our best knowledge, this is the first geometry-based acoustic simulation that offers high fidelity and realism while also being fast enough to use for embodied learning. We showcase the simulator's properties and benchmark its performance against real-world audio measurements. In addition, through two downstream tasks covering embodied navigation and far-field automatic speech recognition, highlighting sim2real performance for the latter. SoundSpaces 2.0 is publicly available to facilitate wider research for perceptual systems that can both see and hear.

Visual Acoustic Matching

We introduce the visual acoustic matching task, in which an audio clip is transformed to sound like it was recorded in a target environment. Given an image of the target environment and a waveform for the source audio, the goal is to re-synthesize the audio to match the target room acoustics as suggested by its visible geometry and materials. To address this novel task, we propose a cross-modal transformer model that uses audio-visual attention to inject visual properties into the audio and generate realistic audio output. In addition, we devise a self-supervised training objective that can learn acoustic matching from in-the-wild Web videos, despite their lack of acoustically mismatched audio. We demonstrate that our approach successfully translates human speech to a variety of real-world environments depicted in images, outperforming both traditional acoustic matching and more heavily supervised baselines.

Multichannel Speech Enhancement without Beamforming

Deep neural networks are often coupled with traditional spatial filters, such as MVDR beamformers for effectively exploiting spatial information. Even though single-stage end-to-end supervised models can obtain impressive enhancement, combining them with a beamformer and a DNN-based post-filter in a multistage processing provides additional improvements. In this work, we propose a two-stage strategy for multi-channel speech enhancement that does not need a beamformer for additional performance. First, we propose a novel attentive dense convolutional network (ADCN) for predicting real and imaginary parts of complex spectrogram. ADCN obtains state-of-the-art results among single-stage models. Next, we use ADCN in the proposed strategy with a recently proposed triple-path attentive recurrent network (TPARN) for predicting waveform samples. The proposed strategy uses two insights; first, using different approaches in two stages; and second, using a stronger model in the first stage. We illustrate the efficacy of our strategy by evaluating multiple models in a two-stage approach with and without beamformer.

TADRN: Triple-Attentive Dual-Recurrent Network for Ad-hoc Array Multichannel Speech Enhancement

Deep neural networks (DNNs) have been successfully used for multichannel speech enhancement in fixed array geometries. However, challenges remain for ad-hoc arrays with unknown microphone placements. We propose a deep neural network based approach for ad-hoc array processing: Triple-Attentive Dual-Recurrent Network (TADRN). TADRN uses self-attention across channels for learning spatial information and a dual-path attentive recurrent network (ARN) for temporal modeling. Temporal modeling is done independently for all channels by dividing a signal into smaller chunks and using an intra-chunk ARN for local modeling and an inter-chunk ARN for global modeling. Consequently, TADRN uses triple-path attention: inter-channel, intra-chunk, and inter-chunk, and dual-path recurrence: intra-chunk and inter-chunk. Experimental results show excellent performance of TADRN. We demonstrate that TADRN improves speech enhancement by leveraging additional randomly placed microphones, even at locations far from the target source. Additionally, large improvements in objective scores are observed when poorly placed microphones in the scene are complemented with more effective microphone positions, such as those closer to a target source.

TPARN: Triple-path Attentive Recurrent Network for Time-domain Multichannel Speech Enhancement

In this work, we propose a new model called triple-path attentive recurrent network (TPARN) for multichannel speech enhancement in the time domain. TPARN extends a single-channel dual-path network to a multichannel network by adding a third path along the spatial dimension. First, TPARN processes speech signals from all channels independently using a dual-path attentive recurrent network (ARN), which is a recurrent neural network (RNN) augmented with self-attention. Next, an ARN is introduced along the spatial dimension for spatial context aggregation. TPARN is designed as a multiple-input and multiple-output architecture to enhance all input channels simultaneously. Experimental results demonstrate the superiority of TPARN over existing state-of-the-art approaches.