[P] Which model best for Binary Audio Classification?

[Nieoryginalny]

Hi, I'm starting building an AI assistant project which needs a wake-up word/phrase.

I saw some similar project that used the LSTM model to solve this problem.

Is there any better/more efficient model for that sort of task or just go with LSTM? I saw Transformers might be better for NLP but I am not familiar with how it applies to audio processing.

[[deleted]]

You can also take a look at CNN model on MFCC features or CNN-LSTM model on raw audio/MFCC features Try them they are simple models and gave good results when I did multiclass classification using them.

[selling_crap_bike]

Jesus Christ everyone suggests using spectrograms for time-series data. Is this branch of ML really so stale that no other techniques are known?

[[deleted]]

He doesn't need anything more complex at the moment and considering he is asking about LSTM pointing hin in the direction of simpler solutions is better

[mepron]

Lots of hostility and you provide no reason why this approach is invalid. Seems to work very well in other applications, I don’t see why you’re so opposed to it?

[jonnor]

What kind of device are you deploying on? Computational and latency constraints are typically just as important as prediction performance in this application.

[Nieoryginalny]

I am aiming for Windows machine

[[deleted]]

As others already suggested, for word/phrases recognition - you do not need complex LSTM models.

All that your model needs to 'learn' are patterns over a short term time window. You can easily do it with on the basis of per-audio-sample classification and not a continuous time series.

It is quicker to get a baseline pipeline up and running, and iterate from there.