Jan Skoglund
Jan Skoglund received his Ph.D. degree from Chalmers University of Technology, Sweden. From 1999
to 2000, he worked on low bit rate speech coding at AT&T Labs-Research, Florham Park, NJ. He was
with Global IP Solutions (GIPS), San Francisco, CA, from 2000 to 2011 working on speech and audio processing tailored for packet-switched networks. GIPS' audio and video technology was found in many deployments by, e.g., IBM, Google, Yahoo, WebEx, Skype, and Samsung.
Since a 2011 acquisition of GIPS he has been a part of Chrome at Google, Inc. He leads a team in San Francisco, CA, developing speech and audio signal processing components for capture, real-time communication, storage, and rendering.
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Binamix -- A Python Library for Generating Binaural Audio Datasets
Dan Barry
Davoud Shariat Panah
Alessandro Ragano
Andrew Hines
AES 158th Audio Engineering Society Convention (2025) (to appear)
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The increasing demand for spatial audio in applications such as virtual reality, immersive media, and spatial audio research necessitates robust solutions to generate binaural audio data sets for use in testing and validation. Binamix is an open-source Python library designed to facilitate programmatic binaural mixing using the extensive SADIE II Database, which provides Head Related Impulse Response (HRIR) and Binaural Room Impulse Response (BRIR) data for 20 subjects. The Binamix library provides a flexible and repeatable framework for creating large-scale spatial audio datasets, making it an invaluable resource for codec evaluation, audio quality metric development, and machine learning model training. A range of pre-built example scripts, utility functions, and visualization plots further streamline the process of custom pipeline creation. This paper presents an overview of the library’s capabilities, including binaural rendering, impulse response interpolation, and multi-track mixing for various speaker layouts. The tools utilize a modified Delaunay triangulation technique to achieve accurate HRIR/BRIR interpolation where desired angles are not present in the data. By supporting a wide range of parameters such as azimuth, elevation, subject Impulse Responses (IRs), speaker layouts, mixing controls, and more, the library enables researchers to create large binaural datasets for any downstream purpose. Binamix empowers researchers and developers to advance spatial audio applications with reproducible methodologies by offering an open-source solution for
binaural rendering and dataset generation. We release the library under the Apache 2.0 License at https://github.com/QxLabIreland/Binamix/
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On the Design of the Binaural Rendering Library for Eclipsa Audio Immersive Audio Container
Tomasz Rudzki
Gavin Kearney
AES 158th Convention of the Audio Engineering Society (2025) (to appear)
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Immersive Audio Media and Formats (IAMF), also known as Eclipsa Audio, is an open-source audio container developed to accommodate multichannel and scene-based audio formats. Headphone-based delivery of IAMF audio requires efficient binaural rendering. This paper introduces the Open Binaural Renderer (OBR), which is designed to render IAMF audio. It discusses the core rendering algorithm, the binaural filter design process as well as real-time implementation of the renderer in a form of an open-source C++ rendering library. Designed for
multi-platform compatibility, the renderer incorporates a novel approach to binaural audio processing, leveraging a combination of spherical harmonic (SH) based virtual listening room model and anechoic binaural filters. Through its design, the IAMF binaural renderer provides a robust solution for delivering high-quality immersive audio across diverse platforms and applications.
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This paper explores the integration of model-based and data-driven approaches within the realm of neural speech and audio coding systems. It highlights the challenges posed by the subjective evaluation processes of speech and audio codecs and discusses the limitations of purely data-driven approaches, which often require inefficiently large architectures to match the performance of model-based methods. The study presents hybrid systems as a viable solution, offering significant improvements to the performance of conventional codecs through meticulously chosen design enhancements. Specifically, it introduces a neural network-based signal enhancer designed to post-process existing codecs’ output, along with the autoencoder-based end-to-end models and LPCNet—hybrid systems that combine linear predictive coding (LPC) with neural networks. Furthermore, the paper delves into predictive models operating within custom feature spaces (TF-Codec) or predefined transform domains (MDCTNet) and examines the use of psychoacoustically calibrated loss functions to train end-to-end neural audio codecs. Through these investigations, the paper demonstrates the potential of hybrid systems to advance the field of speech and audio coding by bridging the gap between traditional model-based approaches and modern data-driven techniques.
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Perceptual Evaluation of a Mix Presentation for Immersive Audio with IAMF
Carlos Tejeda-Ocampo
Toni Hirvonen
Ema Souza-Blanes
Mahmoud Namazi
AES 158th Convention of the Audio Engineering Society (2025) (to appear)
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Immersive audio mix presentations involve transmitting and rendering several audio elements simultaneously. This enables next-generation applications, such as personalized playback. Using immersive loudspeaker and headphone MUSHRA tests, we investigate bitrate vs. quality for a typical mix presentation use case of a foreground stereo element, plus a background Ambisonics scene. For coding, we use Immersive Audio Model and Formats, a recently
proposed system for Next-Generation Audio. Excellent quality is achieved at 384 kbit/s even with reasonable amount of personalization. We also propose a framework for content-aware analysis that can significantly reduce the bitrate when using underlying legacy audio coding instances.
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Perceptual Audio Coding: A 40-Year Historical Perspective
Juergen Herre
Schuyler Quackenbush
Minje Kim
2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2025)
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In the history of audio and acoustic signal processing perceptual audio coding has certainly excelled as a bright success story by its ubiquitous deployment in virtually all digital media devices, such as computers, tablets, mobile phones, set-top-boxes, and digital radios. From a technology perspective, perceptual audio coding has undergone tremendous development from the first very basic perceptually driven coders (including the popular mp3 format) to today’s full-blown integrated coding/rendering systems. This paper provides a historical overview of this research journey by pinpointing the pivotal development steps in the evolution of perceptual audio coding. Finally, it provides thoughts about future directions in this area.
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In this paper, we present SCOREQ, a novel approach for speech quality prediction. SCOREQ is a triplet loss function for contrastive regression that addresses the domain generalisation shortcoming exhibited by state of the art no-reference speech quality metrics. In the paper we: (i) illustrate the problem of L2 loss training failing at capturing the continuous nature of the mean opinion score (MOS) labels; (ii) demonstrate the lack of generalisation through a benchmarking evaluation across several speech domains; (iii) outline our approach and explore the impact of the architectural design decisions through incremental evaluation; (iv) evaluate the final model against state of the art models for a wide variety of data and domains. The results show that the lack of generalisation observed in state of the art speech quality metrics is addressed by SCOREQ. We conclude that using a triplet loss function
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This paper presents NOMAD (Non-Matching Audio Distance), a differentiable perceptual similarity metric that measures the distance of a degraded signal against non-matching references. The proposed method is based on learning deep feature embeddings via a triplet loss guided by the Neurogram Similarity Index Measure (NSIM) to capture degradation intensity. During inference, the similarity score between any two audio samples is computed through Euclidean distance of their embeddings. NOMAD is fully unsupervised and can be used in general perceptual audio tasks for audio analysis e.g. quality assessment and generative tasks such as speech enhancement and speech synthesis. The proposed method is evaluated with 3 tasks. Ranking degradation intensity, predicting speech quality, and as a loss function for speech enhancement. Results indicate NOMAD outperforms other non-matching reference approaches in both ranking degradation intensity and quality assessment, exhibiting competitive performance with full-reference audio metrics. NOMAD demonstrates a promising technique that mimics human capabilities in assessing audio quality with non-matching references to learn perceptual embeddings without the need for human-generated labels.
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In this paper, we present SCOREQ, a novel approach for speech quality prediction. SCOREQ is a triplet loss function for contrastive regression that addresses the domain generalisation shortcoming exhibited by state of the art no-reference speech quality metrics. In the paper we: (i) illustrate the problem of L2 loss training failing at capturing the continuous nature of the mean opinion score (MOS) labels; (ii) demonstrate the lack of generalisation through a benchmarking evaluation across several speech domains; (iii) outline our approach and explore the impact of the architectural design decisions through incremental evaluation; (iv) evaluate the final model against state of the art models for a wide variety of data and domains. The results show that the lack of generalisation observed in state of the art speech quality metrics is addressed by SCOREQ. We conclude that using a triplet loss function
View details
Preview abstract
This paper explores the integration of model-based and data-driven approaches within the realm of neural speech and audio coding systems. It highlights the challenges posed by the subjective evaluation processes of speech and audio codecs and discusses the limitations of purely data-driven approaches, which often require inefficiently large architectures to match the performance of model-based methods. The study presents hybrid systems as a viable solution, offering significant improvements to the performance of conventional codecs through meticulously chosen design enhancements. Specifically, it introduces a neural network-based signal enhancer designed to post-process existing codecs’ output, along with the autoencoder-based end-to-end models and LPCNet—hybrid systems that combine linear predictive coding (LPC) with neural networks. Furthermore, the paper delves into predictive models operating within custom feature spaces (TF-Codec) or predefined transform domains (MDCTNet) and examines the use of psychoacoustically calibrated loss functions to train end-to-end neural audio codecs. Through these investigations, the paper demonstrates the potential of hybrid systems to advance the field of speech and audio coding by bridging the gap between traditional model-based approaches and modern data-driven techniques.
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A High-rate Extension to SoundStream
Andrew Storus
Hong-Goo Kang
Yero Yeh
2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (2023)
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In this paper, we propose a high-rate extension of the SoundStream codec, which is able to generate almost transparent quality audio at 16 kbps for wideband speech signals. SoundStream shows reasonably good performance at low bit-rates (e.g. around 9 kbps), but its performance does not improve much when using more bits for encoding the latent embeddings. Motivated by experimental results showing that neural audio codec performance is highly related to the characteristics of latent embeddings such as dimensionality, dependency, and probability density function shape, we propose a convolutional transformer architecture and an attention-based multi-scale latent decomposition method that significantly enhances codec performance when quantizing high-dimensional embeddings.
Experimental results show the superiority of our proposed model over conventional approaches.
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