I've combed StackOverflow and the web for many questions on whistle detection, etc, and many people did explain as much as they could as to how they can go about detecting their stuff.
capturing sound for analysis and visualizing frequences in android
analyzing whistle sound for pitch note
But what I don't get is how does FFT help you to detect certain sounds in a given sample audio data? Here's what I understand so far from some stuff I found here and there.
-The sine wave is more or less the building block of ALL signals, musical or not
-Three parameters - FREQUENCY, AMPLITUDE, and INITIAL PHASE, characterize every steady sine wave completely.
-They make each and any kind of wave unique.
-Fourier transform can be used to inspect what kinds of sine waves there are in a signal
SOURCE -- [Audio signal processing basics][3]
Audio data that the computer generates as received from the mic or other input source, for live processing, is an array of amplitudes processed (or stored or taken) at a particular sample rate.
So how does one go from that to detecting whistles and claps? And complex things such as say, a short period of whistling to a particular song?
My theory of detecting is that we test our whistles in a spectogram, and record the particular frequency and amplitude characteristics. And then if those particular characteristics are repeated again in the input, we've detected a whistle. Am I right or wrong? This sound processing stuff is a little complicated.
Forgot to mention this - I'm using Python. Java is also okay, since most of the examplar code I found was for Android which is in Java. And I can work in Java too. Any mention of any libraries or APIs would be helpful too.
