Re: Search and remove audio sections

[Jeff Learman <jjlearman@gmail.com>]

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How many is "a bunch"?  Unless you have hundreds, or unless there's some
real obvious audible flag to indicate the ads, it'd be easier to find a
good simple GUI audio editor that lets you simply select and delete the
ads.  My guess is that this really isn't going to be easy to do, and you'll
have to spend the time to audit the results of trials to see how many files
they worked on, and in the end it'll take a lot of time.

On Tue, 17 Nov 2020 at 15:44, Jeremy Nicoll - ml sox users <
jn.ml.sxu.88@wingsandbeaks.org.uk> wrote:

> On 2020-11-17 15:52, Dani wrote:
> > Hi,
> >
> > I have a bunch of old MP3 podcasts that have ads in them, at the
> > beginning and the end. These are short bits of podcasts (up to 10
> > minutes each), and the ads are quite distracting.
> > The ads are about 30 seconds long and usually have a small familiar
> > jingle before they start and after they end.
>
> > I was wondering if there is an ability using SoX (or other tool) to do
> > a "search and remove" on these, in a batch format - that would apply
> > to hundreds of these files.
> > Something in the form of:
> > %jingle% -> the familiar jingle at the start and end of the ad, so...
> > mimicking a made-up wildcard/regex search:
> > Search for:  (%jingle% * %jingle%) ( * ) (%jingle% * %jingle%)
> > Replace: ($2) - meaning - I leave only the center part.
> > Is that something that can be done with audio?
>
> I don't know.
>
> If the jingles at the start and end of each ad are binary equal (which
> they might be if an automated system placed copies of their contents
> in the files) then in theory one could use a conventional file search
> utility to locate each one.
>
> Translating the byte offset from the start of an mp3 into the hh:mm:ss
> (or sample count) position might be complicated, especially if the mp3
> are stored with a variable bit rate rather than a fixed one.
>
> Recognising the jingles might be hard if any aspect of mp3 compression
> of the audio means that successive parts of jingles don't appear in the
> exact same bit- and byte- pattern in each file.
>
> If the files contain, say, continuous music (or maybe even speech) then
> there's a tiny gap (hopefully of digital silence) then a jingle then a
> second tiny gap then more content, I think you could possibly look for
> the positions of the gaps.
>
> If there's no gaps, or if eg the transition from speech or music to
> jingle usually involves a jump in volume you could look for those.
>
> If you knew where they seemed to be you could sanity-check them - ie
> decide that they probably do enclose a jingle if they are (say) between
> 29.3 and 30.7 secs apart.
>
> If that was ok, you could generate "trim" commands to remove them.
>
>
> It might be possible if first you did some fairly extreme eq changes
> on (a copy of) the file, eg to try to make speech sounds very quiet
> but leave music at a higher level, to make it easier to spot the
> transitions.
>
> A quick look at the sox manual suggests that the "silence" or "vad"
> effects, applied creatively (perhaps just to small snippets of each
> file) might also help to identify where things are.  That's because
> if an effect can remove a gap (if there is one) from (say) a 1 sec
> piece of audio, then that's easy to identify. You'd certainly need
> to experiment...
>
>
>
> I've a script (written in oorexx, for use on a Windows system) that
> essentially issues
>
>   sox inputfile outputfile "trim" trimparm "stats"
>
> with trimparm defined to extract eg a 3 second period of the inputfile
> (eg from 15 seconds in, through to one sample less than 18 seconds in)
> then it reads the "stats" output and stores the peak level information.
> It does that for every 3-second period of data between two points in
> the file.  Thus from each short chunk of the file it produces a line
> of information like
>
>                  Pk lev dB     -21.31    -21.31    -24.15
>
> (which you'll see is one line of the stats output, if you look in the
> sox manual)
>
> which is, first the highest level from both/either channel, then the
> highest level from the left channel, then the highest from the right
> channel.  The script also calculates the difference in level between
> the two peak levels.  With a whole set of these values it also tracks
> maximum and minimum values of those.  The result is a file containing eg
>
> (warning these lines might wrap and need copied elsewhere to read them
> more
> easily)
>
> sl hh:mm:ss.fr    hh:mm:ss.fr            Both        Left       Right
>      Diff
> -- -----------    -----------         -------     -------     -------
>   -------
>   1 00:00:00.00 to 00:00:03.00-1s       -33.46      -33.46      -34.27
>      0.81
>   2 00:00:03.00 to 00:00:06.00-1s       -23.06      -23.06      -28.17
>      5.11
>   3 00:00:06.00 to 00:00:09.00-1s       -23.01      -23.01      -25.27
>      2.26
>   4 00:00:09.00 to 00:00:12.00-1s       -16.95      -16.95      -19.82
>      2.87
>   5 00:00:12.00 to 00:00:15.00-1s       -18.51      -18.51      -20.37
>      1.86
>   6 00:00:15.00 to 00:00:18.00-1s       -25.16      -25.16      -26.32
>      1.16
>   7 00:00:18.00 to 00:00:21.00-1s       -22.36      -22.36      -28.50
>      6.14
>   8 00:00:21.00 to 00:00:24.00-1s       -21.64      -21.64      -25.37
>      3.73
>   9 00:00:24.00 to 00:00:27.00-1s       -21.30      -21.30      -24.37
>      3.07
> 10 00:00:27.00 to 00:00:30.00-1s       -22.11      -22.11      -24.41
>      2.30
>
> ...
>
>      Minima:      -36.70      -36.70      -37.60        0.06
>    at slice:          27          27          27          13
>
>
>      Maxima:      -13.84      -13.84      -14.58        6.14
>    at slice:          11          11          11           7
>
>
>     Average:      -23.50      -21.27      -26.06        2.56
>
>
> (where "sl" means "slice"), and in "hh.mm.ss.fr" the "fr" means
> "fraction" of a
> seconds, ie tenths and hundredths)
>
> I wrote this because I was trying to process recordings of a choir, and
> while the stats
> effect applied to each whole song told me that one side of the choir was
> mostly louder
> than the other, this was not true for every song they'd performed.  The
> script to look
> at the situation every 3 secs helped me find out why - for example
> whether applause
> levels or random audience noise were causing peaks that weren't
> characteristic of the
> music.
>
> I picked 3 second slices for no good reason.  One could use every tenth
> of a second but
> then there'd be 30 times more results...   One could possibly use a
> results file like
> this to look for predictable level changes (give or take half a db or
> so).
>
> It just gave me a better idea of what was going on.  However, it was
> also in a format
> that could have been read by another program if it was trying to detect
> moments of
> interest.
>
> --
> Jeremy Nicoll - my opinions are my own
>
>
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>

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