Background

Modern  Hearing Aids
Commercial Hearing Aid

Modern hearing aids contain all manner of signal processing wizardry to assist the impaired listener in various ways. Much effort goes into developing noise-reduction technologies, and microphone array technology coupled with beam-forming algorithms to reduce off-axis sound interference. These may help to improve speech reception, or at least alleviate some of the exhaustion associated with the increased listening effort required from impaired listeners, especially when extracting information from sounds of interest in cacophonous environments. Processing often includes feedback cancellation algorithms to prevent howl associated with high gain settings in conjunction with open (non occluded) fittings. Some hearing aids even transpose information from different frequency bands to others.

At the heart of any hearing aid is the ‘gain model’. The most basic goal of any hearing assistive device is to restore audibility of sounds that were previously inaudible to the hearing-impaired listener. Hearing impaired listeners have a reduced sensitivity to environmental sounds, i.e. they cannot detect the low level sounds that a normal hearing listener would be able to detect, and so it can be said that their thresholds of hearing are relatively high, or raised. To compensate for this deficit, the intensity of the stimulus must be increased, i.e. gain is provided by the hearing aid. The earliest hearing aids (the ear trumpet) just provided gain.

Hearing Aid Trumpet

It is important to note that a flat loss (equal loss of sensitivity across frequency) is not often observed. More commonly, there is a distinct pattern of hearing loss, where the sensitivity is different to that of normal hearing listeners at different frequencies. For a hearing aid to work effectively across the audible spectrum, it must provide differing amounts of gain in different frequency regions. Modern hearing aids decompose sounds into separate frequency bands, perform various processing tasks, then finally recombine the signal into a waveform that can be presented to the listener via a loudspeaker.

Most hearing impaired listeners will begin to experience discomfort from loud sounds at levels not too dissimilar to those with a normal hearing sensitivity. This means that the impaired listener has a reduced dynamic range into which the important sonic information must be squeezed. If the hearing aid applies a linear gain irrespective of the incoming sound intensity, it will help the listener detect quiet sounds, but it will also make loud sounds unbearably loud. For this reason, modern hearing aids also use compression algorithms. A lot of gain is applied to low intensity sounds to help with audibility, while considerably less gain is applied to high intensity sounds, so as not to over-amplify sounds that are already audible to the listener.

The figure below (taken from this open-access publication) is shown help illustrate the concept of reduced dynamic range. It shows categorical loudness scaling (CLS) functions for a hypothetical hearing-impaired listener and a hypothetical normal-hearing listener. A test stimulus is presented at various intensities (represented by the x-axis), and the listener is asked to categorize the loudness on a rating scale (represented by the y-axis). For sounds rated as just audible, there is a large intensity difference between the normal- and impaired-hearing listener data. However, for sounds perceived as very loud, there is little or no difference between the two listeners. The normal-hearing listener’s ratings span a range of approximately 90 dB, whereas the impaired-listener’s ratings span a relatively reduced range of approximately 50 dB.

ategorical loudness scaling

Categorical Loudness Scaling functions for hypothetical normal- and impaired-hearing listeners. Taken from here.

Unfortunately, any non-linear process (including dynamic range compression) applied to the processing chain will have side effects. In order to protect the listener from sudden loud sounds, the compression algorithm needs to respond quickly. However, standard compression algorithms with rapid temporal acuity tend to make the acoustical environment sound distinctly unnatural. The action of the compressor is clearly audible and can interfere with the important information contained in the amplitude modulations of signals such as speech. Fast compression reduces the modulation depth of amplitude modulates signals, and can therefore reduce our ability to extract information from the glimpses of signal information we might otherwise receive during the low intensity dips in modulated masking sounds. Very fast compression also changes the signal to noise ratio (SNR) of steady state signal and noise mixtures. At positive SNRs, the signal is of greater amplitude than the noise signal. If compression is so fast that it works near instantaneously, then the high level peaks of the signal will not be amplified as much as the lower level peaks in the noise signal. The noise level will increase relative to the level of the signal information reducing an otherwise advantageous SNR. The resulting negative impact on speech intelligibility is compounded by any distortion introduced by the compression process. In contrast, slowly acting compression algorithms do not impose so many negative side effects. A very slow compressor acts like a person continuously adjusting the volume control of an amplifier while watching a movie: the gain is increased for the quiet spoken passages, and then decreased in the loud action sequences. This works well for sounds with slowly changing intensity, and the sound ‘quality’ is not vastly altered. However, this is problematic if the volume is cranked up for quiet spoken passages, and there is a sudden intense event in the soundtrack that nearly deafens the audience. For this reason, both fast and slow acting compression algorithms are used in modern hearing aids to get the best possible compromise.

Article Source:  here

 

Blind Source Separation

Blind signal separation, also known as blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process.

A common method for implementing blind source separation is by using an algorithm called FASTICA (Fast Independent Component Analysis). The project will attempt to implement this algorithm to help in the separation of sounds from different sources (separating human voices from background noise) for the hearing aid. Below is the abstract of a research paper that describes in detail how FASTICA is implemented.

A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of nongaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation. In this paper, we present the basic theory and applications of ICA, and our recent work on the subject.

Independent component analysis can be used to separate combined images, MRI data, ECG signals, or in the case of this project, audio signals. This Link provides a demonstration of the FastICA algorithm. Two sounds are mixed, and then separated using the FastICA algorithm.