Bird identification deep neural network using SqueezeNet architecture with simple bypass connections.
The data used in this model is scraped from various open bird database sources.
The data is used as is, meaning no noise reduction of other filtering is applied. Only some of the silent sections of the files are removed before cutting the pieces into 3sec mono audio files.
The splitting is applied with a sliding window of 1.5sec steps. This way more audio samples are generated from each individual file.
The files are then written to a train and test TFRecord-file with split
ratio of 80% for train and 20% for test. With the TFRecord-files it is
possible to minimize the number of readable files and minimize the amount
of IO operations.
After this, the samples are ready for use.
the 3sec files are then fed through a Short Time Fourier Transform (STFT) to generate a spectral representation of the audio, that is suitable for the SqueezeNet.
The data is read by a TFRecordDataset iterator from the TFRecord-file.
This allows nice API for multithreaded preloading and transformation of
the audio files. The code can be found in
generators.py
At first, the network had own STFT-implementation, but due to problems introduced by the freezing and inference optimization of the computation graph the project now uses kapre in performing the needed transformations.
The network implementation can be found in main.py.
It's a basic SqueezeNet that takes in the 3sec audio files and transforms
the files with STFT and injects some additive noise during training (so
that the train examples vary a little always between even epochs).
The current model has been trained for around 20 epochs with Train accuracy of ~0.96 and test accuracy of ~0.84. This is not perfect but it's a start for a quite simple architecture.
I'll try to improve this network at some point. But before optimizing that my priority is to bundle this into an iPhone-app.
You would need a bunch of data first but, yeah:
$ virtualenv -p python3 env
$ source env/bin/activate
$ pip install -r requirements.txt
$ python lib/tfrecord_writer.py
$ python main.py
$ python keras_to_tf.py