Sound detection

About: Sound detection is a research topic. Over the lifetime, 1047 publications have been published within this topic receiving 7957 citations.

Methods and apparatus for targeted sound detection and characterization

Abstract: Sound processing methods and apparatus are provided. A sound capture unit is configured to identify one or more sound sources. The sound capture unit generates data capable of being analyzed to determine a listening zone at which to process sound to the substantial exclusion of sounds outside the listening zone. Sound captured and processed for the listening zone may be used for interactivity with the computer program. The listening zone may be adjusted based on the location of a sound source. One or more listening zones may be pre-calibrated. The apparatus may optionally include an image capture unit configured to capture one or more image frames. The listening zone may be adjusted based on the image. A video game unit may be controlled by generating inertial, optical and/or acoustic signals with a controller and tracking a position and/or orientation of the controller using the inertial, acoustic and/or optical signal.

The Auditory Periphery in Fishes

Abstract: Fossil evidence shows that an inner ear is found in the most primitive of jawless vertebrates (Stensio 1927; Jarvick 1980; Long 1995). It may never be known whether these vertebrates actually were able to “hear” or whether the earliest ear may have been only a vestibular organ for the detection of angular and linear accelerations of the head. However, it is not hard to imagine that such a system could have ultimately evolved into a system for detection of somewhat higher frequency sounds during early vertebrate evolution (Van Bergeijk 1967; Poper and Fay 1997). Although some might argue that sound detection would not have evolved until fish, or predators, started to make sounds, this may not be a valid argument. In fact, it is very likely that the earliest role, and still the most general role, for sound detection is to enable a fish to gain information about its environment from the environment’s acoustic signature (Popper and Fay 1993). Such a signature results from the ways a sound field produced by sources such as surface waves, wind, rain, and moving animals is scattered by things like the water surface, bottom, and other objects.

Method and device for sound detection and audio control

Abstract: At least one exemplary embodiment is directed to a listening device (100) can include a receiver (102) and means for directing a sound produced by the receiver into an ear of the user, a microphone (104) and means for mounting the microphone so as to receive the sound in an environment , detecting means for detecting an auditory signal in the sound received by the microphone, and alerting means for alerting the user to the presence of the auditory signal, whereby the user's personal safety is enhanced due to the user being alerted to the presence of the auditory signal, which otherwise may be unnoticed by the user due to loud sound level created at the ear of the user by the receiver.

A differentially amplified motion in the ear for near-threshold sound detection

Abstract: The ear is a remarkably sensitive pressure fluctuation detector. In guinea pigs, behavioral measurements indicate a minimum detectable sound pressure of ∼20 μPa at 16 kHz. Such faint sounds produce 0.1-nm basilar membrane displacements, a distance smaller than conformational transitions in ion channels. It seems that noise within the auditory system would swamp such tiny motions, making weak sounds imperceptible. Here we propose a new mechanism contributing to a resolution of this problem and validate it through direct measurement. We hypothesized that vibration at the apical side of hair cells is enhanced compared with that at the commonly measured basilar membrane side. Using in vivo optical coherence tomography, we demonstrated that apical-side vibrations peaked at a higher frequency, had different timing and were enhanced compared with those at the basilar membrane. These effects depend nonlinearly on the stimulus sound pressure level. The timing difference and enhancement of vibrations are important for explaining how the noise problem is circumvented.

Automatic adventitious respiratory sound analysis: A systematic review.

Abstract: Background Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established Objective To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds This systematic review provides a complete summary of methods used in the literature to give a baseline for future works Data sources A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases Additional articles were further obtained by references listed in the articles found Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification Study selection Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated Data extraction Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved Data synthesis A total of 77 reports from the literature were included in this review 55 (7143%) of the studies focused on wheeze, 40 (5195%) on crackle, 9 (1169%) on stridor, 9 (1169%) on rhonchi, and 18 (2338%) on other sounds such as pleural rub, squawk, as well as the pathology Instrumentation used to collect data included microphones, stethoscopes, and accelerometers Several references obtained data from online repositories or book audio CD companions Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques Performance reported in the surveyed works were converted to accuracy measures for data synthesis Limitations Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different A standard validation method has not been established, resulting in different works using different methods and performance measure definitions Conclusion A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis The performance of recent studies showed a high agreement with conventional non-automatic identification This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases