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Yes, this is correct. Other values would cause a DC offset.

There are two differences between your problem and music beat detection: The beat in music is a "latent" grid. It's not because a track is at 120BPM that you have exactly 2 notes per second. In musi …

BPM detectors work by finding the strongest period in the onset detection function - a function of time indicating how likely it is that there is an onset at time t. Which brings the topic of onset de …

This is unlikely to be a signal processing question. This is about the internals of the dsPic's firmware libraries and would be more suitable on another stackexchange site. Things that could go wrong …

We assume in this example that the BPM is 120 ; and the audio is processed at a rate of 48kHz. Thus, a beat has a duration of 0.5 seconds or 24000 samples. … To implement the "beat divide" effect, the audio is shoved in a ringer buffer as it is played, and a beat tracker collects the time stamp of beats. …

its value will be copied, thus creating a memory thus it's forbidden with real-time audio? When you pass an argument by value, it is copied onto the stack, so there is no memory allocation. …

If there is some background hum due to AC mains interferences, you can match that with historical records of the fluctuation patterns in mains frequency within the electricity grid. Reference here.

Changing the volume of an audio signal must be done by applying a gain (multiplication) - and optionally clipping if your system has a limited dynamic range. This is as simple as that. … Applying a non-linear function to an audio signal will cause distortion and add harmonics, and you don't want this to happen - you want to modify the loudness of the signal, not its timbre. …

First of all, don't expect an answer like "Compute such and such well-known function on the data and if the result is above such or such value it's tiuuu otherwise it's tou". You won't get anything ro …

Pre-emphasis is a way of compensating for the rapid decaying spectrum of speech. The experiment is worth trying on real data - you will find that the DCT basis is better at extracting a set of decorre …

Between two recordings of the same audio input, but through different microphone/recording equipment? Between two recordings of the same person saying the same word? … The overall audio similarity? Encoding/recording equipment differences? …

You seem to want your comparison to be robust to amplitude changes and time delays, but what about: Audio coding. Should a signal and a MP3 version of itself be considered as similar or different? … Depending on your application (assessing degradations through a signal processing chain, identifying copyrighted work in an audio stream, shazam-like song identification, plagiarism detection on music. …

The target application is automatic singing evaluation. Do you want this to recognize that the lyrics are sung correctly? If not (you don't mind if the people is singing meaningless words, humming, w …

The task of taking a long contiguous audio recording and splitting it up in chunks in which only one speaker is speaking - without any prior knowledge about the voice characteristics of each speaker - …

There is no "universal" window size for audio applications, look at what is used in the paper describing the technique you're implementing. … You can find the list of those filters/thresholds in the source code of an open-source implementation of lastfm's Ke-style audio fingerprinter. …