Hearing loss simulation

What our hearing loss algorithms simulate, with audio examples to illustrate hearing loss.

Our challenge entrants are going to use machine learning to develop better processing of speech in noise (SPIN) for hearing aids. For a machine learning algorithm to learn new ways of processing audio for the hearing impaired, it needs to estimate how the sound will be degraded by any hearing loss. Hence, we need an algorithm to simulate hearing loss for each of our listeners. The diagram belows shows our draft baseline system that was detailed in a previous blog. The hearing loss simulation is part of the prediction model. The Enhancement Model to the left is effectively the hearing aid and the Prediction Model to the right is estimating how someone will perceive the intelligibility of the speech in noise.

The draft baseline system (where SPIN is speech in noise, DRC is Dynamic Range Compression, HL is Hearing Loss, SI is Speech Intelligibility and L & R are Left and Right).

There are different causes of hearing loss, but we’re concentrating on the most common type that happens when you age (presbycusis). Action on Hearing Loss estimate that more than 40% of people over the age of 50 year have a hearing loss, and this rises to 70% of people who are older than 70.

The aspects of hearing loss we’ve decided to simulate are:

  1. The loss of ability to sense the quietest sounds (increase in absolute threshold).
  2. How as an audible sound increases in level, the perceived increase in loudness is greater than normal (loudness recruitment) (Moore et al. 1996).
  3. How the ear has a poorer ability to discriminate the frequency of sounds (spectral smearing).

Audio examples of hearing loss

Here are two samples of speech in noise processed through the simulator. In each audio example there are three repeats of the same sentence:

  1. Unimpaired hearing
  2. Mild hearing impairment
  3. Moderate to severe hearing impairment
0 dB signal to noise ratio

And here is an example where the noise is louder:

Noisier: -10dB signal to noise ratio


The hearing loss model we’re using was generously supplied by Michael Stone at the University of Manchester as MATLAB code and translated by us into Python. (We’ll be making the code available.) The original code was written by members of the Auditory Perception Group at the University of Cambridge, ca. 1991-2013, including Michael Stone, Brian Moore, Brian Glasberg and Thomas Baer. There is no one paper that describes this model, but details can be found in Baer and Moore (1993 and 1994), Moore and Glasberg (1993), and Nejime and Moore (1998).

The original speech recordings come from the ARU corpus, University of Liverpool (Hopkins et al. 2019). This corpus is freely available at the link in the reference below.


Baer, T., & Moore, B. C. (1993). Effects of spectral smearing on the intelligibility of sentences in noise. The Journal of the Acoustical Society of America, 94(3), 1229-1241.

Baer, T., & Moore, B. C. (1994). Effects of spectral smearing on the intelligibility of sentences in the presence of interfering speech. The Journal of the Acoustical Society of America, 95(4), 2277-2280.

Hopkins, C., Graetzer, S., & Seiffert, G. (2019). ARU adult British English speaker corpusof IEEE sentences(ARU speech corpus) version 1.0 [data collection]. Acoustics Research Unit, School of Architecture, University of Liverpool, United Kingdom. DOI: 10.17638/datacat.liverpool.ac.uk/681. Retrieved from http://datacat.liverpool.ac.uk/681/.

Moore, B. C., & Glasberg, B. R. (1993). Simulation of the effects of loudness recruitment and threshold elevation on the intelligibility of speech in quiet and in a background of speech. The Journal of the Acoustical Society of America, 94(4), 2050-2062.

Moore, B. C., Glasberg, B. R., & Vickers, D. A. (1996). Factors influencing loudness perception in people with cochlear hearing loss. B. Kollmeier, World Scientific, Singapore, 7-18.

Nejime, Y., & Moore, B. C. (1998). Evaluation of the effect of speech-rate slowing on speech intelligibility in noise using a simulation of cochlear hearing loss. The Journal of the Acoustical Society of America, 103(1), 572-576.

The baseline

An overview of the current state of the baseline we’re developing for the machine learning challenges

We’re currently developing the baseline processing that challenge entrants will need. This takes a random listener and a random audio sample of speech in noise (SPIN) and passes that through a simulated hearing aid (the Enhancement Model). This improves the speech in noise. We then have an algorithm (the Prediction Model) to estimate the Speech Intelligibility that the listener would perceive (SI score). This score can then be used to drive machine learning to improve the hearing aid.

A talk through the baseline model we’re developing

The first machine learning challenge is to improve the enhancement model, in other words, to┬áproduce a better processing algorithm for the hearing aid. The second challenge is to improve the prediction model using perceptual data we’ll provide.