Bulk download - click topic to download Zip archive of all papers related to that topic: No Subject Listed Acoustics Applications in Audio Diversity Equity and Inclusion Education Immersive & Spatial Audio Perception Recording & Production Recording and Production Room Acoustics Signal Processing Transducers / Converters
An automotive cabin is much smaller than a typical room and contains a combination of reflective and absorptive materials in proximity. As a result, the acoustic response within the cabin sound is initially dense but decays quickly. This creates a dry, unnatural, and less immersive sound field as compared to typical room environments. One approach to address this phenomenon has been to equalize the acoustics of the vehicle cabin and/or introduce artificial reverberation that better matches the desired acoustic properties. However, it is challenging to fully neutralize the acoustics of the cabin due to a dependency on head position and the non-minimum phase behavior within the space. This paper describes a more practical method of morphing the acoustics of the cabin to that of a more desirable space by integrating the reverberation decay characteristics of the vehicle and the desired room through cross-analysis, energy normalization, and frequency spectrum equalization.
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In this work a combination of matrix analysis and a fully coupled FEA is applied to modeling and design of horn drivers. The proposed method makes it possible to calculate the 3-dimensional model of a compression driver that considers all its details and components including glue joints, complex mechanical behaviour of the diaphragm, acoustical properties of compression chamber and the phasing plug. This results in the presentation of a compression driver by four complex matrix coefficients A11, A12, A21, and A22. The method is very efficient in design and optimization of horns and waveguides. Each time a new iteration of horn’s geometry is made, there is no need to run the fully coupled model of the compression driver. Instead, the complex matrix coefficients are used as input parameters for horn modeling.
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Time Series Forecasting (TSF) is used in astronomy, geology, weather forecasting, and finance to name a few. Recent research [1] has shown that, combined with Machine Learning (ML) techniques, TSF can be applied successfully for short-term predictions of music signals. We present here an application of this approach for predicting audio level changes of music and appropriate Dynamic Range Compression (DRC). This ML-based look ahead prediction of audio level allows to apply compression just-in-time, avoiding latency and attack/release time constants, which are proper to traditional DRC and challenging to tune.
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Spatial audio quality assessment is crucial for attaining immersive user experiences, but subjective evaluations are time-consuming and costly. Thus, automated algorithms have been developed for objective quality assessment. This study focuses on the development of an improved binaural perceptual model for spatial audio quality measurement by choosing the best-performing set of design parameters among previously proposed methods. Existing binaural models, particularly extensions of the Perceptual Evaluation of Audio Quality (PEAQ) method, are investigated to enhance spatial audio quality metrics. The performance of the popular Gammatone Filter Bank (GTFB) and PEAQ’s built-in filter bank is compared for its use in constructing spatial distortion metrics related to three binaural cues: inter-aural time and level differences (ITD and ILD) and inter-aural cross-correlation (IACC). Evaluation includes different binaural cue types and window lengths, with subjective scores from a spatial audio quality database used for correlation analysis. Additionally, three binaural cue extraction systems are evaluated using spatial and timbre distortion metrics, employing a common peripheral model. Objective quality scores are derived using multivariate regression and validated against subjective scores from multiple listening test databases. Results indicate similar performance between GTFB and PEAQ’s filter bank in predicting spatial audio quality, making an additional GTFB unnecessary for spatial audio quality assessment. The binaural cue extraction model proposed by Seo et al. (2013) demonstrates the best overall performance. These results can the inform design choices made in developing a binaural model that incorporates higher-level spatial distortion metrics, such as directional loudness. Accurate spatial audio quality metrics can improve the design of spatial processing algorithms for an enhanced immersive user experience.
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The far-field acoustical transfer function of an electroacoustic device, such as a loudspeaker, is of fundamental importance in acoustic modelling software to predict the resulting sound field produced by multiple devices in 3D space. This dataset, commonly known as a directivity balloon, is usually acquired through time-consuming, sophisticated measurement techniques involving extensive hardware and dedicated post-processing algorithms. Additionally, such dataset is usually compensated for the effect of acoustical propagation to reference the magnitude and phase values to conventional distances. This works investigates the effects of propagation compensation within the cylinder measurement method for directivity balloons, specifically considering the use of improper sound speed values relative to the ambient conditions during the measurement process. The importance of employing environmental parameter-dependent models for the propagation speed of sound in this type of measurement is emphasized, as to maintain high accuracy in the final directivity data phase response
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The rapid shift towards Ethernet-based technologies in ProAV systems has exposed signi?cant challenges in the integration and convergence of diverse media transport protocols. Con?icting assumptions among de facto and o?cial standards about underlying network features often result in severe incompatibilities. The situation is exacerbated by the absence of an overarching use case model to guide the development and application of these protocols. To address this, the authors propose a generic use case model grounded in real-world ProAV requirements, especially for live events and productions. This model focuses on system integration and interoperability, taking into account the coexistence of various signal and control tra?c types, loose collaborative teams, and the contrast to traditional IT network design principles. Through this model, the authors aim to clarify requirements, shape realistic expectations for products and implementations, and stimulate discussion for the next evolution in AV networking.
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This paper proposes a new personalized head-related transfer function (HRTF) estimation method based on knowledge distillation (KD). The KD pipeline for generating personalized HRTFs comprises a teacher–student model for transferring well-trained knowledge. The teacher model is the expert that generates personalized HRTFs by representing extensive knowledge using all anthropometric data and ear image. In contrast, the student model is the mimic that attempts to learn from the expert using seven anthropometric data and ear image. The performance of the proposed personalized HRTF estimation approach is evaluated using the Center for Image Processing and Integrated Computing (CIPIC) database. The experiments reveal that the proposed method showed equivalent performance for the root mean squared error and log spectral distance measurements compared to the method using all anthropometric data with the ear images.
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Real-life situations are hard to replicate in the laboratory and often discarded during hearing aids optimisation, leading to performance inconsistencies and user dissatisfaction. As a solution, the authors propose a tool set to incorporate real-life conditions in the design, test and fitting of hearing aids. This tool set includes a spatial audio simulation framework for generating large number of realistic situations, a machine learning algorithm focused on prominent hearing aids problems trained with the newly generated data, and a low-cost spatial audio solution for audiological clinics for improved fitting of hearing aids. The current article presents the first results of the spatial audio simulation framework compared to a reference scenario and other existent solutions in literature. First findings demonstrate that synthesized impulse responses with arbitrary source directivity combined with using hearing aid head related transfer functions, with spatial upsampling and Ambisonic domain optimizations, to generate simulated binaural audio can be a powerful tool for generating several real-life situations for further hearing aids research.
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Classic stereo microphone recording techniques were developed decades ago. These techniques fall within three categories: coincident (XY, Blumlein, M-S), near-coincident (ORTF, NOS, DIN), and spaced (AB, Decca, etc.). All these methodologies rely on the pickup pattern of the microphones used. In fact, the pickup pattern of the microphone is critical for the specific recording technique selected. Recording engineers are well-acquainted with Omni, Cardioid, its variants, and Figure-8 polar patterns. The author hypothesizes that variations in response patterns between different models of microphones, and even among individual microphones themselves, can impact the selection of microphones for stereo recording. Data or spec sheets typically publish the response pattern for a given microphone either at one frequency, or multiple responses for a few selected frequencies. This paper will revisit the traditional stereo microphone techniques, but with the use of dual output microphones, allowing the pickup pattern of each microphone to be determined in post-production. The focus is on near-coincident and coincident pairs as these make the most use of level differences due to the pickup pattern used. The resulting workflow is like that of M-S recording where the level of the Side signal is brought up in the mix. In this case, the levels of the rear capsules are raised until the desired sound is achieved.
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This paper presents Panningtable Synthesis (PTS) as an alternative approach to panning virtual sources in spatial audio that is both a generalization to and more efficient than Vector Base Amplitude Panning (VBAP). This new approach is inspired by a previous technique called Rapid Panning Modulation Synthesis (RPMS). RPMS however exhibits the limitation in that all secondary sources need to be regularly spaced across the circle and organized in equally spaced circles across the sphere. We demonstrate that PTS is not only able to overcome these restrictions, but that it is also fully compliant with VBAP, more computationally efficient and can be regarded as a generalization to the same. Furthermore, we demonstrate that PTS is also able to supersede RPMS both in its capacity to create and shape sound spectra, independently from the number of secondary sources used in the array. Considering creative spatial sound synthesis techniques, PTS can be compared to Wavetable or Wave-Terrain Synthesis, but with the added, inherent spatial characteristics. The flexibility of PTS allows any degree of trade-off between using perceptually correct panning curves and those that target specific sound spectra.
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