The phrase "multiple sound sources" is correct and usable in written English.
It can be used to describe a situation where there are multiple sources of sound, such as in a concert where there are different instruments and microphones producing sound simultaneously. Example: The concert had an impressive audio setup, with multiple sound sources placed strategically around the stage to create an immersive experience for the audience.
We show that linear-threshold frequency integration allows the system to represent multiple sound sources with natural sound localization cues, whereas multiplicative frequency integration does not.
Natural soundscapes often contain multiple sound sources at any given time.
This place-based spectral analysis, or mechanical tonotopy, underlies the brain's ability to distinguish and identify sounds, even when multiple sound sources are present simultaneously.
However, localization of multiple sound sources and static sound source tracking in self-motion are both difficult tasks, especially when the number of sound sources or reflections increase.
In a household where a user's voice and noises emitted from electric appliances originate from arbitrary directions in 3-D space, robots need to recognize the directions of multiple sound sources in order to effectively interact with the user.
Combining studies of auditory perception and computational modeling, Dr. McDermott shows how the brain may take advantage of regularities in how reverberation manifests itself in the complex signal that enters the ear, in order to distinguish multiple sound sources, often referred to as the Cocktail Party problem.
Now, we conclude in two dominant sound sources area, cross-correlation vector will have two dominant values and therefore equal count dominant values for more than two dominant sound sources signals as multiple power spectrum peaks in DOA-based multiple sound source beamforming methods [16].
In the presence of multiple independent sound sources, the distribution can be modeled as mixtures of circular Cauchy distributions and a uniform distribution.
We exploit a statistical reverberation model enabling the definition of a compact "reverberation fingerprint" for characterization of the local environment and computationally efficient data-driven reverberation rendering for multiple virtual sound sources.
In this paper, a methodology for partial field decomposition is presented that is based on the beamforming algorithm for the application of acoustical holography to the composite sound fields generated by multiple incoherent sound sources.
The key difficulty is that operating in everyday listening conditions entails processing a speech signal that is reverberantly mixed into a noise background composed of multiple competing sound sources.
