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· 3 min read
Trevor Cox

An explanation of the time and computational limits for the first round of the enhancement challenge.

The 1st Clarity Enhancement Challenge

For a hearing aid to work well for users, the processing needs to be quick. The output of the hearing aid should be produced with a delay of less than about 10 ms. Many audio processing techniques are non-causal, i.e., the output of the system depends on samples from the future. Such processing is useless for hearing aids and therefore our rules include a restriction on the use of future samples.

The rules state the following:

  • Systems must be causal; the output at time t must not use any information from input samples more than 5 ms into the future (i.e., no information from input samples >t+5ms).
  • There is no limit on computational cost.

· 4 min read
Trevor Cox

Improving hearing aid processing using DNNs blog. A suggested approach to overcome the non-differentiable loss function.

The aim of our Enhancement Challenge is to get people producing new algorithms for processing speech signals through hearing aids. We expect most entries to replace the classic hearing aid processing of Dynamic Range Compressors (DRCs) with deep neural networks (DNN) (although all approaches are welcome!). The first round of the challenge is going to be all about improving speech intelligibility.

Setting up a DNN structure and training regime for the task is not as straightforward as it might first appear. Figure 1 shows an example of a naive training regime. An audio example of Speech in Noise (SPIN) is randomly created (audio sample generation, bottom left), and a listener is randomly selected with particular hearing loss characteristics (random artificial listener generation, top left). The DNN Enhancement model (represented by the bright yellow box) then produces improved speech in noise. (Audio signals in pink are two-channel, left and right because this is for binaural hearing aids.)

schematic

Figure 1

Next the improved speech in noise is passed to the Prediction Model in the lime green box, and this gives an estimation of the Speech Intelligibility (SI). Our baseline system will include algorithms for this. We’ve already blogged about the Hearing Loss Simulation. Our current thinking is that the intelligibility model will be using a binaural form of the Short-Time Objective Intelligibility Index (STOI) [1]. The dashed line going back to the enhancement model shows that the DNN will be updated based on the reciprocal of the Speech Intelligibility (SI) score. By minimising (1/SI), the enhancement model will be maximising intelligibility.

· 3 min read
Trevor Cox

The Clarity Project is based around the idea that machine learning challenges could improve hearing aid signal processing. After all this has happened in other areas, such as automatic speech recognition (ASR) in the presence of noise. The improvements in ASR have happened because of:

  • Machine learning (ML) at scale – big data and raw GPU power.
  • Benchmarking – research has developed around community-organised evaluations or challenges.
  • Collaboration has been enabled by these challenges, allowing working across communities such as signal processing, acoustic modelling, language modelling and machine learning

We’re hoping that these three mechanisms can drive improvements in hearing aids.

Components of a challenge

There needs to be a common task based on a target application scenario to allow communities to gain from benchmarking and collaboration. Clarity project’s first enhancement challenge will be about hearing speech from a single talker in a typical living room, where there is one source of noise and a little reverberation.

We’re currently working on developing simulation tools to allow us to generate our living room data. The room acoustic will be simulated using RAVEN and the Hearing Device Head-related Transfer Functions will come from Denk’s work. We’re working on getting better, more ecologically valid speech than is often used in speech intelligibility work.

baseline

Entrants are then given training data and development (dev) test data along with a baseline system that represents the current state-of-the-art. You can find a post and video on the current thinking on the baseline here. We’re still working on the rules stipulating what is and what is not allowed (for example, will entrants be allowed to use data from outside the challenge).

Clarity’s first enhancement challenge is focussed on maximising the speech intelligibility (SI) score. We will evaluate this first through a prediciton model that is based on a hearing loss simulation and an objective metric for speech intellibility. Simulation has been hugely important for generating training data in the CHIME challenges and so we intend to use that approach in Clarity. But results from simulated test sets cannot be trusted and hence a second evaluation will come through perceptual tests on hearing impaired subjects. However, one of our current problems is that we can’t bring listeners into our labs because of COVID-19.

We’ll actually be running two challenges in roughly parallel, because we’re also going to task the community to improve our prediction model for speech intelligibility.

We’re running a series of challenges over five years. What other scenarios should we consider? What speech? What noise? What environment? Please comment below.

Acknowledgements

Much of this text is based on Jon Barker’s 2020 SPIN keynote

· One min read
Trevor Cox

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

The baseline

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.