Changes between Initial Version and Version 1 of AudioVolume


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Timestamp:
Aug 4, 2017, 2:03:47 PM (7 weeks ago)
Author:
slhck
Comment:

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  • AudioVolume

    v1 v1  
     1= Audio Volume Manipulation = 
     2 
     3== Changing volume == 
     4 
     5To change the audio volume, you may use FFmpeg's [http://ffmpeg.org/ffmpeg.html#volume volume] audio filter. 
     6 
     7If we want our volume to be half of the input volume: 
     8{{{ 
     9ffmpeg -i input.wav -filter:a "volume=0.5" output.wav 
     10}}} 
     11 
     12150% of current volume: 
     13{{{ 
     14ffmpeg -i input.wav -filter:a "volume=1.5" output.wav 
     15}}} 
     16 
     17You can also use decibel measures: 
     18{{{ 
     19ffmpeg -i input.wav -filter:a "volume=10dB" output.wav 
     20}}} 
     21 
     22== Peak and RMS Normalization == 
     23 
     24To normalize the volume to a given peak or RMS level, the file first has to be analyzed using the `volumedetect` filter: 
     25 
     26{{{ 
     27ffmpeg -i input.wav -filter:a volumedetect -f null /dev/null 
     28}}} 
     29 
     30Read the output values from the command line log: 
     31 
     32{{{ 
     33[Parsed_volumedetect_0 @ 0x7f8ba1c121a0] mean_volume: -16.0 dB 
     34[Parsed_volumedetect_0 @ 0x7f8ba1c121a0] max_volume: -5.0 dB 
     35... 
     36}}} 
     37 
     38... then calculate the required offset, and use the `volume` filter as shown above. 
     39 
     40To automate this process and run it on multiple files (including video), you can also use the [https://github.com/slhck/ffmpeg-normalize ffmpeg-normalize Python script] via `pip install ffmpeg-normalize`. For the supported options, see `ffmpeg-normalize -h`. 
     41 
     42== Loudness Normalization == 
     43 
     44If you want to normalize the (perceived) loudness of the file, use the [http://ffmpeg.org/ffmpeg-filters.html#loudnorm loudnorm] filter, which implements the EBU R128 algorithm: 
     45 
     46{{{ 
     47ffmpeg -i input.wav -filter:a loudnorm output.wav 
     48}}} 
     49 
     50This is recommended for most applications, as it will lead to a more uniform loudness level compared to simple peak-based normalization.