I am planning to develop a music app which includes a function to find the similar song just like what KKBOX and Shazam are doing, but I'm not familiar in this area. I've found that they applied FFT to proceed the comparison of the songs so that the user can search the similar song.
However, i am thinking that what if I generate the waveform of the song, and then directly compare the waveform of the songs. I would like to ask is it possible for my idea?
As your objective is to find "similar" songs, comparing a 2d waveform is highly unlikely to work. However, it's a good idea to first explore the feasibility of your approach, before rejecting it out of hand.
I would suggest picking a set of 5 songs
1 song and 1 song you think is very similar to it
1 song that's different from the first one, and a song by the same band on the same album (or from the same time period)
1 audio file that's completely different (e.g. from an audiobook or podcast)
Run through the librosa tutorials (https://librosa.org/doc/main/tutorial.html) and/or some of the walkthroughs on Medium (e.g. https://towardsdatascience.com/extract-features-of-music-75a3f9bc265d), but stopping before you get to the part that uses MFCC. Just focus on the waveform images.
Looking at the visualizations for your songs and thinking through this problem, reason about a)why the waveform-comparison ought to work, and b)why the waveform-comparison won't work.
So think about things like tempo, timbre and timing - what would be the effect on the waveform of playing the same song on different instruments, with a different effects treatment, at a different tempo, or in a different order (same song, but changing order of verses and chorus).
Setting aside the non-trivial quetion of which waveform you'd be using (amplitude? of what frequency/frequencies?), at this point, you should see how many problems there are with just looking at the waveform, and why MFCC (or similar) is better. Additionally, you'll be better prepared to think about how MFCC parameters might be selected - how much of the song do you need to sample, when should you start the sampling.
Is your idea possible? Probably not in the way you are thinking - maybe you could experiment with something like transforming the data of the song in some way and then comparing that representation (e.g. looking at changes in amplitude or tempo) The problem with audio is that it encapsulates a lot of features in its signal:
key
tempo
effects treatment (e.g. reverb)
instruments
tone
dynamics
etc.
Watch a tutorial on audio mixing and you'll see/hear just how much the output signal of the exact same song can be changed without actually changing the song being played.
Innovation sometimes emerges when curious people try things that 'probably won't work', so anything is worth a shot, but once you've figured out for yourself why something won't work, it's useful to accept commonly used techniques, and look for opportunities for innovation in other ways.
Related
I'm currently researching an problem regarding DOA (direction of arrival) regression for an audio source, and need to generate training data in the form of audio signals of moving sound sources. In particular, I have the stationary sound files, and I need to simulate a source and microphone(s) with the distances between them changing to reflect movement.
Is there any software online that could potentially do the trick? I've looked into pyroomacoustics and VA as well as other potential libraries, but none of them seem to deal with moving audio sources, due to the difficulties in simulating the doppler effect.
If I were to write up my own simulation code for dealing with this, how difficult would it be? My use case would be an audio source and a microphone in some 2D landscape, both moving with their own velocities, where I would want to collect the recording from the microphone as an audio file.
Some speculation here on my part, as I have only dabbled with writing some aspects of what you are asking about and am not experienced with any particular libraries. Likelihood is good that something exists and will turn up.
That said, I wonder if it would be possible to use either the Unreal or Unity game engine. Both, as far as I can remember, grant the ability to load your own cues and support 3D including Doppler.
As far as writing your own, a lot depends on what you already know. With a single-point mike (as opposed to stereo) the pitch shifting involved is not that hard. There is a technique that involves stepping through the audio file's DSP data using linear interpolation for steps that lie in between the data points, which is considered to have sufficient fidelity for most purposes. Lot's of trig, too, to track the changes in velocity.
If we are dealing with stereo, though, it does get more complicated, depending on how far you want to go with it. The head masks high frequencies, so real time filtering would be needed. Also it would be good to implement delay to match the different arrival times at each ear. And if you start talking about pinnas, I'm way out of my league.
As of now it seems like Pyroomacoustics does not support moving sound sources. However, do check a possible workaround suggested by the developers here in Issue #105 - where the idea of using a time-varying convolution on a dense microphone array is suggested.
I am tasked with something seemingly trivial which is to
find out how "noisy" a given recording is.
This recording came about via a voice recorder, a
OLYMPUS VN-733 PC which was fairly cheap (I am not doing
advertisement, I merely mention this because I in no way
aim to do anything "professional" here, I simply need to
solve a seemingly simple problem).
To preface this, I have already obtained several datasets
from different outside locations, in particular parks or
near-road recordings. That is, the noise that exists at
these specific locations, and to then compare this noise,
on average, with the other locations.
In other words:
I must find out how noisy location A is compared to location
B and C.
I have made 1 minute recordings each so that at the
least the time span of a recording can be compared
to the other locations (and I was using the very
same voice record at all positions, in the same
height etc...).
A sample file can be found at:
http://shevegen.square7.ch/test.mp3
(This may eventually be moved lateron, it just serves as
example how these recordings may sound right now. I am
unhappy about the initial noisy clipping-sound, ideally
I'd only capture the background noise of the cars etc..
but for now this must suffice.)
Now my specific question is, how can I find out how "noisy"
or "loud" this is?
The primary goal is to compare them to the other .mp3
files, which would suffice for my purpose just fine.
But ideally it would be nice to calculate on average
how "loud" every individual .mp3 is and then compared
it to the other ones (there are several recordings
per given geolocation, so I could even merge them
together).
There are some similar questions but not one in particular
that I was able to find that could answer this in a
objective manner, or perhaps I did not understand the
problem at hand. I have all the audio datasets already
but I have no idea how to find out how "loud" any one
of them is individually; there are some apps on smartphones
that claim that they can do this automatically but since
I do not have any smartphone, this is a dead end for me.
Any general advice will be much appreciated.
Noise is a notion difficult to define. Then, I will focus on loudness.
You could compute the energy of each files. For that, you need to access the samples of the audio signal (generally from a built-in function of you programming language). Then you could compute the RMS energy of the signal.
That could be the more basic processing.
Is it possible with FFT to find a drum solo, or a drum break, in an audio file? Is this something FFT is able to do and are there any resources online that could aid me with learning?
In general, a FFT is not a good choice for detecting the onset of percussion sounds:
An FFT is always calculated over a window of samples (in effect a period of time) and yields the magnitude of signal within the bin and its phase offset. You can therefore determine that there is signal at that particular bin, but not its onset time. The best time resolution available is the window period. Of course, you can make the period shorter at the expense of frequency resolution.
Percussion sounds tend to look like noise and spread across the spectrum. This would be OK if you only had percussions sounds, but is not great in real-life polyphonic content.
However, you might be able to find some inference from the different characteristics of the spectra of a drum solo vs instrumental sections of a track.
The problem of finding the time at which percussion sounds start in music is described in academic journals as onset dectection and is one of the many techniques used for feature extraction; the wider field is known as Music Information Retrieval. Your problem sounds like one of identifying sections in audio files and this might be described as partitioning
A good place to start is Sonic Visualiser which is a tool written specifically for MIR applications. Plug-ins exist for various types of feature extraction. From these you will be able to easily find the large body of academic work in this area. There is an added bonus that the existing plug-ins are all open source too.
I'd look here, there was a bit of discussion with great pointers on the Gamedev SE: https://gamedev.stackexchange.com/questions/9761/beat-detection-and-fft :-)
I'm struggling to choose between a vast number of audio programming languages and APIs. I'm very (totally) new to audio programming so please bear with me.
Software
I need to be able to:
Alter volume of different sounds before outputting them to anything (these sounds can have a variety of different origins, for example mp3s and microphone input)
phase shift sounds
superimpose sounds that I have tweaked (as per items 1 and 2)
control the output to each of 8 channels independently of one another
make this all happen on Windows7
These capabilities need be abstracted by a graphical frontend I will probably make myself. What I want to be able to do is create 'sound sources' and move them around a 3D environment along either pre-defined trajectories and/or in relation to the movement of whoever is inside the rig. The reason I want to do pitch bending is so I can mess with red-shift stuff.
I don't want to have to construct full tracks before-hand and just play them. I want the sound that is played to depend on external input from sensors as well as what I am doing on the frontend.
As far as I know this means I cant use any existing full audio making app.
The Question
I've been looking around for for the API or language I should use and I have not turned up a blank, quite the opposite actually. I'm struggling to narrow down my search. A lot of my problem stems from the fact that I have no experience in audio programming.
So, does anyone know off-hand of an API or language that meets my criteria?
Hardware stuff and goals
(I left this until last because I'm not sure how relevant it is)
My goal is to make three rings of speakers at different heights and to have enough control over them to be able to simulate any number of 'sound sources' within the array. The idea is to have someone stand in the middle of the rig and be able to make it sound like there are lots of things moving around them. To get this working I'm planning on doing a little trig and using 8 channels of audio from my PC. The maths is pretty straight forward, it just the rest that I need to worry about
What I want to do next is attach a bunch of cameras to the thing and do some simple image recognition stuff to be able to 'attach sound sources' to different objects. Eg. If someone is standing in the right place it can be made to seem as though all red balls quack like a duck, and all orange ones moan hauntingly.
This is not to detract from Richard Small's answer, but to comment on some of the other options out there:
If you are looking for something higher-level with which you can prototype and develop this faster, you want max/msp or it's open source competitor puredata. These are designed for musicians who are technically minded, but not so much for programmers. As a result, you can build this sort of thing quickly and efficiently.
You also have some lower level options: PortAudio can handle your audio I/O, you would have to do the sound generation and effects and so on on your own or with other libraries. Cinder and OpenFramewoks both provide interfaces for audio, cameras, and other stuff for "creative programming". I'm afraid I don't know if they meet your full requirements, but they are powerful and popular for this sort of thing so I encorage you to look at them.
The two major ones these days tend to be
WWise
WWise Download Link
FMOD
FMOD Download Link
These two engines may even in fact be overkill for what you need, but I can almost guarantee that they will be capable of anything you require.
I did some of my own research and found out that SID-chips had only few hardware supported synthesizing features. Including three audio oscillators with four possible waveforms (sawtooth, triangle, pulse, noise), with ADSR envelopes and ring modulators. Accompanied with oscillator sync and ring modulators. Also read there was a way to play single PCM sound as well.
It is all so little, but still I heard lots of different sounds from my TV sets. How were they combined to produce all that variety of audio?
To give some specifics, I'd like to know how to combine those components to produce guitar, piano or drum -like audio? Another interesting things would be different buzzes and sounds specific to C64.
I used to write music on the C64 for games, demos and even services (I wrote the official QuantumLink theme, even). As for your question, the four different waveforms were typically overlaid with the sync and ring mods (less often ring, because it was unpredictable on different versions of the SID chip), and sometimes used cleanly.
For example, a typical 'snare' sound would be composed of a noise waveform with a very fast attack and sustain, and depending on whether you wanted a drumstick or brush sound, either a very fast decay and moderately short release, or a short decay and slower release.
Getting the right sound was typically trial and error, and the limitations were pretty heavy. You really never got to the point of piano or guitar sound due to the simple waveforms without overlayable harmonic waveforms, about the best you could get was things that sounded beepy, things that sounded marimba-y, and things that sounded like a snare drum.
One of the tricks used most often to extend sound was done with fast machine code playback routines that could change the played notes on voices so quickly as to give the impression of a fuller, harmonic tone. We just called it arpeggiation, although at 10 to 12 note changes a second it sounded more like a buzzy chord.
As for the sampled waveforms, they were only available as single bit and later 4 bit samples. These sounded terrible despite our best attempts, because basically the method of playback for a sample on the 64 was to play a white noise waveform and rapidly alter the volume on the SID chip to produce a rising and falling wave. Do it fast enough and it sort of sounds like the original sound, poorly tuned in on a staticky radio.
I suggest you grab hold of a C64 emulator for the PC (CCS64 is a good one) and a 64 BASIC programming guide and just play around.... the SID chip is entirely manipulatable from BASIC.
To sum up, how did we get all of those piano and guitar sounds on a C64? We didn't, really.
Take a look at some of these docs related to producing music on the C64:
http://sid.kubarth.com/articles.html
This type of music you are describing falls into the category of "chiptunes". I'd recommend checking out some modern trackers like MilkyTracker, which are used to create music in this style. There are libraries like libmodplug that allow you to play tracker in your software.
Check out some of the C64 emulators out there. I've read that some of them are 100% accurate in ther sound reproduction, true to the original.