Ambisynth

Introduction In my previous post I showed that SampleRNN seems to perform well with 10-hour datasets. So naturally I wanted to try some more datasets of this length. Experiments Birds The rain dataset from last time produced some nice birdsong. So I found the video shown in Example 1, which contains 10 hours of birdsong. Example 1: Video containing 10 hours of non-repetitious birdsong, used to train SampleRNN. I used that to train SampleRNN for 120&nbsp000 iterations (a day and a half). Some of the results are in Example 2; Example 2: Sounds generated by WaveRNN trained for 120 000 iterations on a 10-hour bird dataset. Fire I found several long recordings of fire sounds on Youtube, shown in Example 3. Example 3: Videos containing a total of 12 hours of fire recordings, used to train SampleRNN. One must be careful when selecting videos from Youtube, because many nominally 10 hour recordings are actually 1 hour of unique audio looped 10 ti

Introduction I was listening to this stuff generated by SampleRNN. It seems almost unreasonably good compared to some of the results I have been getting. The most obvious difference is that that person is using 30 to 50 hours of training data, as compared to my typical 1 hour. To a certain extent I have been avoiding using huge datasets, because in a way Ambisynth makes more sense if we can use small datasets -- i.e. if you have 50 hours of soundscapes, why synthesize more? So for now, this post represents more of a (forbidden) research question: how good can synthesized soundscapes be; let's assume practical considerations will take care of themselves at some point in the future. Dataset So I made a 10.5-hour dataset with audio from this amazing website (it's well worth a good listen): https://recordingsofnature.wordpress.com . The recordings were all made with stereo microphones attached to trees in the woods near lakes between 3 and 5 in the morning. I chose several f

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