Anjali Menon

Wen Chin Huang, Dejan Markovic, Alexander Richard et al.

In this work, we present an end-to-end binaural speech synthesis system that combines a low-bitrate audio codec with a powerful binaural decoder that is capable of accurate speech binauralization while faithfully reconstructing environmental factors like ambient noise or reverb. The network is a modified vector-quantized variational autoencoder, trained with several carefully designed objectives, including an adversarial loss. We evaluate the proposed system on an internal binaural dataset with objective metrics and a perceptual study. Results show that the proposed approach matches the ground truth data more closely than previous methods. In particular, we demonstrate the capability of the adversarial loss in capturing environment effects needed to create an authentic auditory scene.

Payal Mohapatra, Calvin Murdock, Ali Aroudi et al.

Many individuals struggle to understand conversation partners in noisy settings, particularly amid background speakers or due to hearing impairments. Emerging wearables like smartglasses offer a transformative opportunity to enhance speech from conversation partners. Crucial to this is identifying the direction in which the user wants to listen, which we refer to as the user's acoustic zones of interest. While current spatial audio-based methods can resolve the direction of vocal input, they are agnostic to listening preferences and have limited functionality in noisy settings with interfering speakers. To address this, behavioral cues are needed to actively infer a user's acoustic zones of interest. We explore the effectiveness of head-orienting behavior, captured by Inertial Measurement Units (IMUs) on smartglasses, as a modality for localizing these zones in seated conversations. We introduce HALo, a head-orientation-based acoustic zone localization network that leverages smartglasses' IMUs to non-invasively infer auditory zones of interest corresponding to conversation partner locations. By integrating an a priori estimate of the number of conversation partners, our approach yields a 21% performance improvement over existing methods. We complement this with CoCo, which classifies the number of conversation partners using only IMU data, achieving 0.74 accuracy and a 35% gain over rule-based and generic time-series baselines. We discuss practical considerations for feature extraction and inference and provide qualitative analyses over extended sessions. We also demonstrate a minimal end-to-end speech enhancement system, showing that head-orientation-based localization offers clear advantages in extremely noisy settings with multiple conversation partners.

Pranay Manocha, Anurag Kumar, Buye Xu et al.

Subjective evaluations are critical for assessing the perceptual realism of sounds in audio-synthesis driven technologies like augmented and virtual reality. However, they are challenging to set up, fatiguing for users, and expensive. In this work, we tackle the problem of capturing the perceptual characteristics of localizing sounds. Specifically, we propose a framework for building a general purpose quality metric to assess spatial localization differences between two binaural recordings. We model localization similarity by utilizing activation-level distances from deep networks trained for direction of arrival (DOA) estimation. Our proposed metric (DPLM) outperforms baseline metrics on correlation with subjective ratings on a diverse set of datasets, even without the benefit of any human-labeled training data.