This AI Can Separate Speech, Music and Sound Effects from Movie Soundtracks

Have you ever tuned in to a video or a TV show and the actors were completely inaudible, or the music was way too loud? Well, this problem, also called the cocktail party problem, may never happen again. Mitsubishi and Indiana University just published a new model as well as a new dataset tackling this task of identifying the right soundtrack. For example, if we take the same audio clip we just ran with the music way too loud, you can simply turn up or down the audio track you want to give more importance to the speech than the music.

The problem here is isolating any independent sound source from a complex acoustic scene like a movie scene or a youtube video where some sounds are not well balanced.

Sometimes you simply cannot hear some actors because of the music playing or explosions or other ambient sounds in the background.

Well, if you successfully isolate the different categories in a soundtrack, it means that you can also turn up or down only one of them, like turning down the music a bit to hear all the other actors correctly.

This is exactly what the researchers achieved. Learn more in the video!

►Read the full article:
https://www.louisbouchard.ai/isolate-voice-music-and-sound-effects-with-ai/
►Petermann, D., Wichern, G., Wang, Z., & Roux, J.L. (2021). The
Cocktail Fork Problem: Three-Stem Audio Separation for Real-World
Soundtracks. https://arxiv.org/pdf/2110.09958.pdf
►Project page: https://cocktail-fork.github.io/
►DnR dataset: https://github.com/darius522/dnr-utils#overview
►My Newsletter (A new AI application explained weekly to your emails!):
https://www.louisbouchard.ai/newsletter/

00:01

have you ever tuned in to a video or a

00:03

tv show and the sound was like this

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where the actors are completely

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inaudible or something like

00:14

this where the music is way too loud

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well this problem also called the

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cocktail party problem may never happen

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again mitsubishi and indiana university

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just published a new model as well as a

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new data set tackling this task of

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identifying the right soundtrack for

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example if we take the same audio clip

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we just ran with the music way too loud

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you can simply turn up or down the audio

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attack you want to give more importance

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to the speech than the

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music and straight into the hot pans the

00:46

problem here is isolating any

00:48

independent sound source from a complex

00:50

acoustic scene like a movie scene or

00:52

youtube video where some sounds are not

00:55

well balanced sometimes you simply

00:56

cannot hear some actors because of the

00:58

music playing or explosions or other

01:01

ambient sounds in the background well if

01:03

you successfully isolate the different

01:05

categories in a soundtrack it means that

01:07

you can also turn up or down only one of

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them like turning down the music a bit

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to hear all the other actors correctly

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as we just did from someone that isn't a

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native english speaker this will be

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incredibly useful when listening to

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videos with loud background music and

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actors or speakers with a strong accent

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i am not used to just imagine having

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these three sliders in a youtube video

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to manually tweak them how cool would

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that be

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you have a metal arm it could also be

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incredibly useful for translations or

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speech-to-speech applications where we

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could just isolate the speaker to

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improve the task's results here the

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researchers focused on the task of

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splitting a soundtrack into three

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categories music speech and sound

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effects three categories that are often

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seen in movies or tv shows they called

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this task the cocktail fork problem and

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you can clearly see where they got the

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name from and i'll spoil you the results

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they are quite amazing as we will hear

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in the next few seconds but first let's

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take a look at how they receive a movie

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soundtrack and transform it into three

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independent soundtracks this is the

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architecture of the model you can see

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the input mixture y which is the

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complete soundtrack at the top and at

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the bottom all of our three output

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sources x which i repeat are the speech

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music and other sound effects separated

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the first step is to encode the

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soundtrack using a fourier transform on

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different resolutions called stft or

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short time fourier transform this means

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that the input which is a soundtrack

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having frequencies over time is first

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split into shorter segments for example

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here it is either split with 32 64 or

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256 milliseconds windows then we compute

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the fourier transform on each of these

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shorter segments sending 8 milliseconds

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at a time for each window or segment

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this will give the fourier spectrum of

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each segment analyzed on different

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segment sizes for the same soundtrack

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allowing us to have short-term and

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long-term information on the soundtrack

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by emphasizing specific frequencies from

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the initial input if they appear more

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often in a longer segment for example

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this information initially represented

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in time frequency is now replaced by the

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fourier phase and magnitude components

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or fourier spectrum which can be shown

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in a spectrogram similar to this one

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note that here we have only an

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overlapping segment of 0.10 seconds but

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it is the same thing in our case with

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three different segment sizes also

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overlapping then this transformed

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representation simply containing more

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information about the soundtrack is sent

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into a fully connected block to be

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transformed into the same dimension for

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all branches this transformation is the

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first module which is learned during

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training of the algorithm we then

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average the results as it is shown to

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improve the model's capacity to consider

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these multiple sources as a whole rather

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than independently here the multiple

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sources are the transformed soundtrack

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using differently sized windows don't

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give up yet we just have a few steps

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left before hearing the final results

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this average information is then sent

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into a bidirectional long short-term

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memory which is a type of recurrent

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neural network allowing the model to

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understand the inputs over time just

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like a convolutional neural network

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understands images over space if you are

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not familiar with recurrent neural

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networks i invite you to watch the video

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i made introducing them this is the

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second module that is trained during

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training we average the results once

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again and finally send them to each of

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our three branches that will extract the

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appropriate sounds for the category here

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the decoder is simply fully connected

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layers again as you can see on the right

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they will be responsible for extracting

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only the wanted information from our

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encoded information of course this is

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the third and last module that learns

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during training in order to achieve this

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and all these three modules are trained

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simultaneously finally we just reverse

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the first step taking the spectrum data

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back into time frequency components and

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voila we have our final soundtrack

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divided into three categories as i said

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earlier in the video this research

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allows you to turn up or down the volume

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of each category independently but an

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example is always better than words so

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let's quickly hear that on two different

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clips

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[Music]

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i phil swift here for flex tape the

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super strong waterproof tape

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that could instantly patch bonds

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as if it wasn't already cool enough the

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separation also allows you to edit

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specific soundtrack imminently to add

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some filters or reverb we're strong and

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once it's on it holds on tight and for

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emergency auto repairs

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it's correct even in the toughest

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conditions

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they also released the data set for this

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new test by merging three separate data

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sets one for speech one for music and

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another for sound effects this way they

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created soundtracks from which they

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already had their real separated auto

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channels and could train their model to

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replicate this ideal separation of

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course the merging or mixing steps

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wasn't as simple as it sounds they had

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to make the final soundtrack as

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challenging as a real movie scene this

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means that they had to make

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transformations to the independent audio

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tracks to have a good blend that sounds

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realistic in order to be able to train a

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model on this data set and then use it

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in the real world i invite you to read

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their paper for more technical detail

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about their implementation and this new

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data set they introduced if you'd like

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to tackle this task as well if you do so

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please let me know and send me your

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progress i'd love to see that or rather

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to hear that both are linked in the

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description below thank you very much

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for watching for those of you who are

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still here and huge thanks to anthony

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manilow the most recent youtube members

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supporting the videos