![single gunshot sound effect mp3 single gunshot sound effect mp3](https://soundcamp.org/sounds/381/kick/A/house-kick-drum-single-shot-a-key-646-4Dy-waveform.png)
![single gunshot sound effect mp3 single gunshot sound effect mp3](https://i.ytimg.com/vi/iNu5km7UdqM/mqdefault.jpg)
So I can imagine that some kind of averaging on every loop would cause this to take considerably longer. I should also note this script takes just over 1 minute to process the 3 hour file (which includes 237,426,624 samples).
#Single gunshot sound effect mp3 mp4
This does not work with the MP3 files above, but did with an MP4 version - where it was able to find the sample I extracted, but it was only that one sample (not all 12). Sample_series = numpy.around(sample_series, decimals=5) įor source_id, source_sample in enumerate(source_series): Source_series = numpy.around(source_series, decimals=5) Sample_series, sample_rate = librosa.load('sample.mp3') # 1 second file Source_series, source_rate = librosa.load('source.mp3') # 3 hour file I'm very new to audio processing, but my initial thought was to extract a sample of the 1 second sound effect, then use librosa in python to extract a floating point time series for both files, round the floating point numbers, and try to get a match. The time offsets will be stored in the ID3 Chapter Frame MetaData.Įxample Source, where the sound effect plays twice.įfmpeg -ss 0.9 -i source.mp3 -t 0.95 sample1.mp3 -acodec copy -yįfmpeg -ss 4.5 -i source.mp3 -t 0.95 sample2.mp3 -acodec copy -y The sound effect is similar every time, but because it's been encoded in a lossy file format, there will be a small amount of variation. Is it possible to identify each time this sound effect is played, so I can note the time offsets? I have a load of 3 hour MP3 files, and every ~15 minutes a distinct 1 second sound effect is played, which signals the beginning of a new chapter.