4 posts tagged with "baseline"

We are pleased to announce the release of the 2nd Clarity Enhancement Challenge (CEC2) baseline system code.

The baseline code has been released in the latest commit to the Clarity GitHub repository.

The baseline system perform NAL-R amplification according to the audiogram of the target listener, followed by a simple gain control and output of the signals to 16-bit stereo wav format. The system has been kept deliberately simple with no microphone array processing or attempt at noise cancellation.

HASPI scores for the dev set have been measured. The scores are as follows.

See here for further details.

If you have any problems using the baseline code please do not hesitate to contact us at claritychallengecontact@gmail.com, or post questions on the Google group.

Some comments on signal alignment and level-insensitivity​

Our baseline binaural speech intelligibility measure in round one is the Modified Binaural Short-Time Objective Intelligibility measure, or MBSTOI. This short post outlines the importance of correcting for delays that your hearing aid processing algorithm introduces into the audio signals to allow MBSTOI to estimate the speech intelligibility accurately. It also discusses the importance of considering the audibility of signals before evaluation with MBSTOI.

Evaluation​

In stage one, entries will be ranked according to the average MBSTOI score across all samples in the evaluation test set. In the second stage, entries will be evaluated by the listening panel. There will be prizes for both stages. See this page for more information.

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). RNID (formerly Action on Hearing Loss) estimate that more than 40% of people over the age of 50 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 (impaired frequency selectivity).

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 produce a better processing algorithm for the hearing aid. The second challenge is to improve the prediction model using perceptual data we’ll provide.