The Journal of the Audio Engineering Society — the official publication of the AES — is the only peer-reviewed journal devoted exclusively to audio technology. Published 10 times each year, it is available to all AES members and subscribers.
The Journal contains state-of-the-art technical papers and engineering reports; feature articles covering timely topics; pre and post reports of AES conventions and other society activities; news from AES sections around the world; Standards and Education Committee work; membership news, new products, and newsworthy developments in the field of audio.
Authors:Gu, Jun; Shen, Yong; Feng, Xuelei
Affiliation:Key Laboratory of Modern Acoustics (MOE), Institute of Acoustics, Nanjing University, Nanjing, 210093, China
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Authors:Kamaris, Gavriil; Zachos, Panagiotis; Mourjopoulos, John
Affiliation:Audio and Acoustic Technology Group, Wire Communications Laboratory, Electrical and Computer Engineering Dept., University of Patras, Greece
This work examines the feasibility and acceptability of digitally correcting the response irregularities of typical earphones used for listening with cell phones and other mobile devices. A novel adaptive low filter order response equalization method is introduced since for these applications digital signal processing resources are limited; hence such filters are restricted in order. The method employs parallel infinite impulse response (IIR) filter sections with 5–9 pole pairs that match well a target frequency response known to be suitable for earphone listening. The tests with earphones of varying specifications and price indicate that these short filters can be effective for response equalization and that such processing improves listener preference.
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Author:Tamer, Yahya Burak
Affiliation:Faculty of Communication, Bahçesehir University, Istanbul, Turkey
Digital streaming has become the most popular way to experience music today. Loudness normalization applied by streaming services allows for the restoration of dynamic contrast, yet the hypercompressed production trends sustain. This paper studies current dynamic and spectral tendencies through integrated loudness and long-term average spectrum (LTAS) analyses of popular music playlists offered by digital streaming services. Deviations from the large-scale spectral characterizations provided by earlier studies on popular music LTAS were investigated and an increase in the slope of the presence band was observed. The paper concludes with recommendations for the optimization of the presence band using digital filtering via metadata during streaming playback.
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Authors:Chojnacki, Bartlomiej; Terry Cho, Sang-Ik; Mehra, Ravish
Affiliation:Facebook Reality Labs, Redmond, Washington, United States; AGH University of Science and Technology, Cracow, Poland; Facebook Reality Labs, Redmond, Washington, United States; Facebook Reality Labs, Redmond, Washington, United States
Head-related transfer-functions (HRTF) are a central part of spatializing audio. However measuring the near-field HRTF at close source distances presents unique challenges. In particu-lar the existing sound sources designed to be appropriate for near-field HRTF measurements on human subjects exhibit a notable issue of being unable to generate a sufficient acoustic output level at lower frequencies (below 300 Hz) while keeping a proper omnidirectional directivity pattern at higher frequencies. This paper proposes a novel design to overcome this limitation of low-frequency range. Several aspects of the design were considered in the paper: type of enclosure, low-frequency extension, choice of transducers, and metrics for sound source assessment. The chosen solutions are discussed together with numerical and experimental verification. The source constructed under the design method and process described herein achieved a frequency range of 120–16,000 Hz for which it can be used to measure HRTFs at source distances as small as 0.15 m from the subject’s head.
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Authors:Sridhar, Rahulram; Choueiri, Edgar Y.
Affiliation:3D Audio and Applied Acoustics Laboratory, Princeton University, Princeton, New Jersey 08544, USA
A formula is derived for computing the order at which the infinite series representation of the rigid-sphere head-related transfer function (RS-HRTF) must be truncated to minimize the time required to compute the HRTF to a sufficiently high accuracy based on binaural perception metrics. Quick and accurate computation of this HRTF may be useful for implementing spatial audio in computationally limited and portable devices. Using a brute-force approach, the lowest truncation order, Nmin, that yields the RS-HRTF that differs from the benchmark (i.e., the RS-HRTF computed with the highest possible accuracy) by less than just-noticeable difference thresholds in interaural time and level differences is approximately computed for a wide range of source distances. By fitting power and rational functions to these computed values, a formula that approximates Nmin as a function of frequency and source distance is derived. It is shown that truncation order varies significantly with source distance and that the proposed formula, unlike a previous one, accurately captures this variation. Consequently, using the proposed formula instead of the previous one results in a more accurate RS-HRTF that is also computed 48% faster on average.
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Affiliation:Constantine the Philosopher University in Nitra, Nitra, Slovakia
Technical ear training is currently gaining more and more attention during the training of professional sound engineers. In recent years, a number of web applications and standalone programs have been created to provide a basic training interface for ear training. However, with the greater accessibility of audio plug-ins and the rising demand to use them in production, the question arises as to how these tools could be integrated into the ear training process. The aim of this article is to point out the specifics and possibilities of using audio plug-ins as training tools through the unique prototype of a proprietary, standalone host.
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Digital audio processing is increasingly dependent on algorithms that learn various features and characteristics of audio signals. Neural networks are often used, and they have to be trained with large bodies of audio material so that they can start to behave in a predictable and useful way. Once trained they can be put to work in roles such as distinguishing between live and studio recordings, searching for specific drum sounds in mixes, creating morphed sounds, or emulating existing analog effects.
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