Stefano Damiano, Kathleen MacWilliam, Valerio Lorenzoni, Thomas Dietzen, Toon van Waterschoot
{"title":"The trajectoRIR database: room acoustic recordings along a trajectory of moving microphones.","authors":"Stefano Damiano, Kathleen MacWilliam, Valerio Lorenzoni, Thomas Dietzen, Toon van Waterschoot","doi":"10.1186/s13636-026-00449-2","DOIUrl":"10.1186/s13636-026-00449-2","url":null,"abstract":"<p><p>Data availability is essential in the development of acoustic signal processing algorithms, especially when it comes to data-driven approaches that demand large and diverse training datasets. For this reason, an increasing number of databases have been published in recent years, including either room impulse responses (RIRs) or audio recordings during motion. In this paper we introduce the trajectoRIR database, an extensive, multi-array collection of both dynamic and stationary acoustic recordings along a controlled trajectory in a room. Specifically, the database contains moving-microphone recordings and stationary RIRs that spatially sample the room acoustics along an L-shaped trajectory. This combination makes trajectoRIR unique and applicable to a wide range of tasks, including sound source localization and tracking, spatially dynamic sound field reconstruction, auralization, and system identification. The recording room has a reverberation time of <math> <mrow><mrow><mn>0.5</mn></mrow> <mspace></mspace> <mtext>s</mtext></mrow> </math> , and the three different microphone configurations employed include a dummy head, with additional reference microphones located next to the ears, 3 first-order Ambisonics microphones, two circular arrays of 16 and 4 channels, and a 12-channel linear array. The motion of the microphones was achieved using a robotic cart traversing a <math> <mrow><mrow><mn>4.62</mn></mrow> <mspace></mspace> <mtext>m</mtext></mrow> </math> -long rail at three speeds: [0.2, 0.4, 0.8] m/s. Audio signals were reproduced using two stationary loudspeakers. The collected database features 8648 stationary RIRs, as well as perfect sweeps, speech, music, and stationary noise recorded during motion. Python functions are provided to access the recorded audio and retrieve the associated geometric information.</p>","PeriodicalId":521327,"journal":{"name":"Journal on audio, speech, and music processing","volume":"2026 1","pages":"19"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13061821/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147680315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Niloofar Omidi Piralideh, Philippe Gournay, Roch Lefebvre
{"title":"Zero-bit self-adaptive spectral noise shaping for speech and audio coding.","authors":"Niloofar Omidi Piralideh, Philippe Gournay, Roch Lefebvre","doi":"10.1186/s13636-025-00441-2","DOIUrl":"10.1186/s13636-025-00441-2","url":null,"abstract":"<p><p>In speech and audio coding, quantization noise, also known as coding noise, is often modeled as additive white noise. This noise can cause noticeable distortions, particularly in frequency regions where human hearing is most sensitive. Noise shaping, a key technique in speech and audio coding, addresses this issue by redistributing the noise spectrum to less perceptible frequency regions, leveraging auditory masking effects to enhance perceived audio quality. This paper introduces a novel approach to perceptually shape the spectrum of coding noise in speech and audio codecs. The proposed approach is based on the well-known principle of pre- and post-processing with adaptive linear filters, but features a new method for estimating filter coefficients. Typically, these coefficients are estimated either from the current input to the pre-processor (forward adaptation) or from the past synthesized signal (backward adaptation). In contrast, the proposed approach estimates the pre-processing filter coefficients directly from the current input to the pre-processor, while the post-processing filter coefficients are estimated from the current input to the post-processor. This estimation approach eliminates the need for transmitting information and introduces no lag between the frame used for estimation and the frame being filtered. Because the adaptation is achieved without any increase in bitrate, the method is referred to as \"zero-bit.\" To function correctly, some constraints must be imposed on calculating the pre-processor filter coefficients. An implementation of this new approach, termed \"constrained adaptation,\" is described. Subjective evaluation results demonstrate that constrained adaptation performs at least as well as forward or backward adaptation in shaping coding noise, with no cost in bit rate or temporal lag.</p>","PeriodicalId":521327,"journal":{"name":"Journal on audio, speech, and music processing","volume":"2026 1","pages":"12"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13019801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147577598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Hi-Audio online platform for recording and distributing multi-track music datasets.","authors":"José M Gil Panal, Aurélien David, Gaël Richard","doi":"10.1186/s13636-026-00459-0","DOIUrl":"10.1186/s13636-026-00459-0","url":null,"abstract":"<p><p>This paper introduces the Hi-Audio online platform, an open-source tool designed to support musicians and researchers in the field of Music Information Retrieval (MIR). The platform enables the recording, uploading, and sharing of multitrack musical compositions, aiming to build an open-access audio database to advance research in music technology. Uploaded audio files are automatically analyzed upon synchronization with the server, leveraging signal processing techniques and machine learning models to generate rich metadata. The platform facilitates remote and asynchronous collaboration via a web-based interface accessible at hiaudio.fr. Furthermore, a novel built-in method for accurate and robust round-trip latency estimation in the browser is proposed and integrated into the platform, demonstrating its applicability in real-world distributed recording scenarios. Finally, an initial user evaluation with musicians was conducted to assess usability and practical relevance under realistic usage conditions. The evaluation combined task-based performance analysis with standardized usability and workload measures. The results indicate high task completion rates for core recording functions and show that the platform can be used effectively by musicians with minimal prior training.</p>","PeriodicalId":521327,"journal":{"name":"Journal on audio, speech, and music processing","volume":"2026 1","pages":"26"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13161328/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}