pitch-tracking

With limited resources such as slower CPUs, code size and RAM, how best to detect the pitch of a musical note, similar to what an electronic or software tuner would do? Should I use: Kiss FFT FFTW Discrete Wavelet Transform autocorrelation zero crossing analysis octave-spaced filters other? In a nutshell, what I am trying to do is to recognize a single musical note, two octaves below middle-C to two octaves above, played on any (reasonable) instrument. I'd like to be within 20% of the semitone - in other words, if the user plays too flat or too sharp, I need to distinguish that. However, I

I'm new to this field - but I need to perform a WAV-to-MIDI conversion in java. Is there a way to know what exactly are the steps involved in WAV-to-MIDI conversion? I have a very rough idea as in you need to; sample the wav file, filter it, use FFT for spectral analysis, feature extraction and then write the extracted features on to MIDI. But I cannot find solid sources or papers as in how to do all that? Can some one give me clues as in how and where to start? Are there any Open Source APIs available for this WAV-to-MIDI conversion process? Advance thanks It's a more involved process than

I'm familiar with Computer Vision ( Well, know OF it ), of which one application can be image recognition, such as Optical Character Recognition , I believe. However, something that I am more interested in is 'computer listening', which I have just learned is considered Digital Signal Processing . The thing that interests me the most about signal processing is the potential application in music. I remember a while ago I saw a preview of an application ( Sorry, forgot the name ) which could listen to a recording of someone playing a guitar, and automatically graph it out across a time-line with

Whats a good digital signal processing algorithm that is good on guitar chords? Since Fast Fourier Transform I think only is accurate on single notes played on the guitar but not notes that are played simultaenously (i.e. chords). Thanks! tias The short answer is that you need much more than one algorithm. Good chord recognition methods could more aptly be described as "systems", but usually they are indeed based on an initial transform to the frequency domain (most often DFT). If you want a chord representaton of the song similar to this C G Am F7 F6 C ... then this is actually a problem that

I'm trying to do real-time pitch detection using C++. I'm testing some code from performous ( http://performous.org/ ), because everything else hasn't worked for me. I know for sure that this works, but i just cant get it to work. I've been trying this for a few weeks now, and I haven't been able to get any pitch detection code working. Instead of using input from the mic, you should create data of a known single frequency and run that through the program and see if it gets you the correct result. Then you can add harmonics to it and see if that works. Real world data is just too variable for

There's this tech-festival in IIT-Bombay, India, where they're having an event called "Artbots" where we're supposed to design artbots with artistic abilities. I had an idea about a musical robot which takes a song as input, detects the notes in the song and plays it back on a piano. I need some method which will help me compute the pitches of the notes of the song. Any idea/suggestion on how to go about it? This is exactly what I'm doing here as my last year project :) except one thing that my project is about tracking the pitch of human singing voice (and I don't have the robot to play the

I have a sample held in a buffer from DirectX. It's a sample of a note played and captured from an instrument. How do I analyse the frequency of the sample (like a guitar tuner does)? I believe FFTs are involved, but I have no pointers to HOWTOs. endolith The FFT can help you figure out where the frequency is, but it can't tell you exactly what the frequency is. Each point in the FFT is a "bin" of frequencies, so if there's a peak in your FFT, all you know is that the frequency you want is somewhere within that bin, or range of frequencies. If you want it really accurate, you need a long FFT