Detecting the fundamental frequency
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?
Answer
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 tune)
The quickest way I can think of is to utilize BASS library. It contains ready-to-use function that can give you FFT data from default recording device. Take a look at "livespec" code example that comes with BASS.
By the way, raw FFT data will not enough to determine fundamental frequency. You need algorithm such as Harmonic Product Spectrum to get the F0.
Another consideration is the audio source. If you are going to do FFT and apply Harmonic Product Spectrum on it. You will need to make sure the input has only one audio source. If it contains multiple sources such as in modern songs there will be to many frequencies to consider.
Harmonic Product Spectrum Theory
If the input signal is a musical note, then its spectrum should consist of a series of peaks, corresponding to fundamental frequency with harmonic components at integer multiples of the fundamental frequency. Hence when we compress the spectrum a number of times (downsampling), and compare it with the original spectrum, we can see that the strongest harmonic peaks line up. The first peak in the original spectrum coincides with the second peak in the spectrum compressed by a factor of two, which coincides with the third peak in the spectrum compressed by a factor of three. Hence, when the various spectrums are multiplied together, the result will form clear peak at the fundamental frequency.
Method
First, we divide the input signal into segments by applying a Hanning window, where the window size and hop size are given as an input. For each window, we utilize the Short-Time Fourier Transform to convert the input signal from the time domain to the frequency domain. Once the input is in the frequency domain, we apply the Harmonic Product Spectrum technique to each window.
The HPS involves two steps: downsampling and multiplication. To downsample, we compressed the spectrum twice in each window by resampling: the first time, we compress the original spectrum by two and the second time, by three. Once this is completed, we multiply the three spectra together and find the frequency that corresponds to the peak (maximum value). This particular frequency represents the fundamental frequency of that particular window.
Limitations of the HPS method
Some nice features of this method include: it is computationally inexpensive, reasonably resistant to additive and multiplicative noise, and adjustable to different kind of inputs. For instance, we could change the number of compressed spectra to use, and we could replace the spectral multiplication with a spectral addition. However, since human pitch perception is basically logarithmic, this means that low pitches may be tracked less accurately than high pitches.
Another severe shortfall of the HPS method is that it its resolution is only as good as the length of the FFT used to calculate the spectrum. If we perform a short and fast FFT, we are limited in the number of discrete frequencies we can consider. In order to gain a higher resolution in our output (and therefore see less graininess in our pitch output), we need to take a longer FFT which requires more time.