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Practical Audio Interfacing
Published in Francis Rumsey, John Watkinson, Digital Interface Handbook, 2013
Francis Rumsey, John Watkinson
Because of the variety of sampling rates in use in digital audio and the increasing use of audio sample resolutions beyond 16 bits it is important to ensure that the audio signal retains optimum sound quality when it is converted digitally from one rate or resolution to another. The question of sample rate conversion has already been covered to some extent earlier in this chapter, since it is closely related to the topic of synchronization. There is little more to be said here except to state that normally it is impossible to interconnect two devices digitally whose sampling rates differ by more than a tiny amount from one another, requiring that a sample rate convertor be used between the two. The question of differences in sample word length, though, will be covered in more detail.
Fundamentals of human response to sound
Published in Frank Fahy, John Walker, Fundamentals of Noise and Vibration, 2003
Sound quality is a general term which describes those features of a sound which contribute to its subjective appraisal which arc not well described by simple objective measures such as A-wcighted sound level. Sounds can have almost infinite variation in spectral and temporal characteristics and many of these differences can be separately perceived, such as the degree of tonality or impulsivity. In practice, this means that some sounds can be rated as noisier or more annoying than others even where the overall level or the amount of physical energy present is actually lower. This means that any product labelling scheme based on A-weighted sound level or sound power level alone will not tell the whole story as far as consumers are conccrned.
Vehicle Interior Sound Classification Based on Local Quintet Magnitude Pattern and Iterative Neighborhood Component Analysis
Published in Applied Artificial Intelligence, 2022
Erhan Akbal, Turker Tuncer, Sengul Dogan
Vehicle interior sound classification (VISC) can be used for many purposes. These are robotic, information security, cybercrime, traffic accident detection, vehicle type classification. Computer-aided sound classification systems are capable of extracting many features from audio signals. However, the human auditory system can detect sounds in a range of limited frequency. Computer-aided automatic sound classification systems use all values in signal frequency. Therefore, high accurate automated sound classification methods have been proposed by using computer-aided systems. Our primary motivation is to define a novel sound classification study area by using vehicles. Humans spend a long time in vehicles, and vehicles can be utilized in a crime area. Therefore, vehicle type classification is crucial for sound forensics and machine learning. Detection of vehicle type from interior car sounds It can be used for digital forensics examination. Sound files are frequently encountered in digital materials obtained from criminals. The resulting sound files are usually analyzed by manual methods. It is important to obtain information about the wanted criminal from the sound files. The method we suggest will be able to detect which type of tool the criminal is using by using sound files. It can also be used to detect noise and sound quality interior the vehicle. Interior vehicle sound classification model, which we recommend for quality detection and estimation, can be used. In order to classify vehicle types, a VISC dataset is collected, and a novel VISC model is presented in this work. Also, our second motivation is to propose a sound classification based biometric identification method for vehicles. By using this method, sound classification based vehicle sound quality systems can also be developed.
Clinical efficiency and safety of the oticon medical neuro cochlear implant system: a multicenter prospective longitudinal study
Published in Expert Review of Medical Devices, 2020
David Schramm, Joseph Chen, David P. Morris, Nael Shoman, Daniel Philippon, Per Cayé-Thomasen, Michel Hoen, Chadlia Karoui, Ariane Laplante-Lévesque, Dan Gnansia
The Neuro One sound processor, available since 2015, offers two coding strategies: CRYSTALIS (default) and Main Peak Interleaved Sampling (MPIS). Both CRYSTALIS and MPIS are multiband spectral extraction strategies. Such strategies are also called ‘n-of-m’ as they select ‘n’ frequency channels, out of ‘m’ available, with the highest spectral energy in each stimulation cycle. CRYSTALIS has a high pitch frequency filtering mechanism to provide as much information as possible to the patient. MPIS stimulates a pre-selected maximum number of electrodes per acquisition frame (i.e., an anti-crosstalk function minimizes interaction between electrodes, so two adjacent electrodes are not stimulated at the same time). Both strategies can be combined with either Coordinated Adaptive Processing (CAP; default) or a multi-band compression function (XDP). CAP uses continuous automated environment detection to coordinate the selection of signal processing strategies such as Free Focus, Voice Track, Voice Guard, and Wind Noise. Free Focus applies automatic adaptive directionality with the aim to improve speech intelligibility in noise. Voice Track applies Wiener filter-based multiband single-channel noise reduction to improve speech intelligibility in noise as well as sound quality [8]. Voice Guard applies adaptive back-end dynamic compression in four frequency bands. The amount of compression applied is a function of the electric dynamic range of the CI patient. The input/output function is always compressive, and the amount of compression increases above the knee point. The knee point of each frequency band is constantly adjusted so that 95% of the frequency band’s sound intensity falls under its knee point. Knee points are mapped to 75% of the patient’s electric dynamic range. The wide input dynamic range improves identification of both soft and loud speech compared to logarithmic wide-band compression [9]. Wind Noise applies a dual-microphone wind noise reduction algorithm with the aim to improve listening comfort. DigiMap 4.0 software is used to map the Neuro One.