# Tag Info

14

This is a well-studied problem, dating back from the mid 90s (DARPA/NIST broadcast transcription challenges). Search for "speech/music segmentation" or "audio segmentation" and you'll find thousands of research papers. There are two broad approaches to solve this problem: Supervised classification Train a speech/music classifier, using a standard machine ...

9

In designing such transformations, one should take into account competing interests: fidelity to the human auditory system (that varies with people), including non-linear or even chaotic aspects (tinnitus) easiness of the mathematical formulation for the analysis part possibility to discretize it or allow fast implementations existence of a suitable stable ...

7

TL;DR: Subspaces are low-dimensional, linear portions of the entire signal space that are expected to contain (or be close to) a large part of the observable and useful signals or transformations thereof, with additional tools that allow us to compute interesting things on the data We are given a set of data. To manipulate them more easily, it is common ...

6

The matlab codes that implemented D. Ellis's algorithm are on their website: http://labrosa.ee.columbia.edu/projects/beattrack/

6

Since the notes from a guitar are no pure sinusoids, you should expect to see some harmonics, even when analyzing the dry signal without effects. E.g., the note E is the perfect fifth of the note A, i.e., it is the second harmonic. If you use distortion or modulation effects (chorus, flanger, and phaser) you get even more additional frequencies due to the ...

6

Yeah some of us can do it, you can speed up or slow down without affect the pitch, some guys call this applications of Time Stretch, there different ways to do it, you can do in frequency domain or time domain, you will need choose what is best for you, you will find some advantages and disadvantages of each. Time Domain: In Time Domain you can try some ...

6

Depends to some degree on the instrument: electronic versus mechanical, string versus reed versus pipe, etc. Typical electronic keyboards have no coupling between keys, and the DSP mixer is usually very close to linear (other than quantization effects, etc., unless the level is high enough to cause clipping, or kick in an AGC). But guitars and pianos do ...

5

You probably need more than one algorithm for an accurate detection. Work on frequency domain (mostly via DFT and spectrogram) is the most often used initial transform. After that sophisticated probabilistic models originally used in speech recognition are applied, such as hidden Markov models, dynamic Bayesian networks, and conditional random fields. The ...

5

The two concepts are related to two different dimensions or aspects of music which might or might not be correlated. Onset detection is concerned with finding the points in time at which sounds start. Doing this does not require prior knowledge of the particular pitch (or fundamental frequency) of the sound. It may indeed rely on the property that at the ...

5

I've had a try at reproducing the effect, and I think these are some of the key elements: You need a high-resolution FFT (large size; windows overlapping in time so that this doesn't come at the expense of frame rate) and to discard all but the lowest-frequency bins. You can tell this from the video because there are only rarely any harmonics visible and ...

5

Subspaces are a Linear Algebra concepts. The best representative example I can think of is the relationship of the XY plane to XYZ space, The former is a subspace of the latter. Any vector in the plane also lies in the space. Every vector in space has an orthogonal projection onto the subspace. So a set of vectors in your subspace can only reach vectors ...

4

The up-sampling process will always change the signal in some measurable way. However, if it's done properly the changes are negligible and don't result it any audible difference. Most commercially sample rate converters (hardware or software implementations), do a really good job at this. Off course, if done badly, upsampling can result in clearly audible ...

4

If your square wave has a mean of zero (this is important!), then a simple accumulator can do the job. Its operation is described by $$y[n]=x[n]+y[n-1]$$ where $x[n]$ is the input (square wave) and $y[n]$ is the output (triangular wave). This is a simple Matlab/Octave script showing how it works: sq = [1,-1,1,-1,1,-1,1,-1]'*ones(1,5); sq = sq'(:); ...

4

The CZT allows for a fairly general evaluation of the Z transform - the more general evaluation path looks like a spiral, so it has a radial component step size as well an angular step size.For spectral zooming, you're only using a subset of this. You're evaluating around the unit circle and only for a small set of frequencies. The Zoom FFT can be ...

4

The tool/theory you describe is really a large area of research in music technology, broadly called audio time-scale modification. A large component of this field is how you might prevent audible changes to frequency following time stretching. This can be approached with both frequency- and time-domain methods, depending on the constraints or goals of your ...

4

Yeah, notation is not ideal. It is not - he assumes that each of the $M$ antenna elements is connected to its own RF chain, i.e., there are also $M$ receivers available. If you have fewer receivers you need to modify your $A$, it needs to contain the response your $K$ receivers observe given a wave from a certain direction. He doesn't put it but yeah, $F$ ...

3

I realized I had forgotten to make an update to this. I ended up following @robert bristow-johnson's recommendations in the comments. I used time-domain McLeod Pitch Method in a live application (records audio in a loop) which works very well. You can see the source code here: https://github.com/sevagh/Pitcha

3

This depends highly on the signal and it's content. For narrow band signal, the loudness can fairly well be estimated through the equal loudness curves as published in ISO 226 (see for example) http://en.wikipedia.org/wiki/Equal-loudness_contour For wide band signal, things are more complicated. If the signals are stationary, you apply frequency weighting ...

3

I would suggest you to take a closer look on this publications: MATCH: A Music Alignment Tool Chest Live Tracking of Musical Performances Using on-line Time Warping Shortly speaking, algorithm is following:: Extract temporal features of your signals (Audio Spectral Flatness, MFCC's, Onset, etc.). Using Dynamic Time Warping with some constraints,...

3

the etymology of the word "note" as it applies to music is simply the notation, the note that a composer would make to paper to represent a particular action taken by the musician performing the music. like "taking notes". normally in audio-to-MIDI conversion, a musical note is something that can be represented with a pair of MIDI Note-On and Note-Off ...

3

there are a lotta people doing research regarding music synthesis and DSP. yes you can sample notes and different key velocities and play them back with different MIDI velocity parameters. if you wanted to somehow interpolate between a note played at $mf$ to $f$ and on to $ff$ and $fff$urther, you would need to find a way to phase align the waveforms. IMO ...

3

This part of that paper suggests the answer: The issue being that the standard FFT just does linearly-spaced spectral samples from $-f_s/2$ to $f_s/2$ where $f_s$ is the sampling frequency. The CZT allows for arbitrary selection of the sampled points by selection of f2 and f1 in w. And, as @johnnymopo says below, the CZT is not limited to the unit circle:...

3

The prime thing such algorithms aim to do is to make use of more information that you may have about the signal. In this case, the extra information is that you know the number of signals (sinusoids) present in your measurements. One pro for both is, therefore, when your measurements match the assumption, you get a more accurate representation of the ...

3

Here are some possibilities: Transverse waves on a guitar string have two polarized components, one parallel and one perpendicular to the guitar's soundboard. User hotpaw2 mentioned energy exchange. It is imaginable that energy transfer could happen between the components. The component parallel to the soundboard is largely inaudible, so it could stealthily ...

3

Audio quality assessment is one of the most critical pieces of audio coding and enhancing applications. The task requires an accurate and objective (mathematical) modeling of human auditory system including its subjective virtues. However, the task of subjective quality assessment is one of the most complex problems to be attacked on Earth. Currently all ...

3

The basic idea is called - Music Finger Printing. Searching for it will yield many results. I'm attaching few good ones I found: How Does Shazam Work? How Does Shazam Work? Music Recognition Algorithms, Fingerprinting and Processing. Creating Your Own Shazam (Identify Songs) with Python Through Audio Fingerprinting (The YouTube Video). Audio ...

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Frequency is mathematically defined as the number of cycles per second. So it is a more strict word mathematically. It is represented numerically by the unit called Hertz. $f=1/T$, where $T$ represents the one-period length of a waveform. This makes frequency quantifiable. Pitch on the other hand, is a perceptual characteristic of a sound frequency, so it'...

3

A subspace is just a vector space that's included in a bigger vector space. Separating a random signal space into two statistically uncorrelated subspaces, a desired signal space and a noise space, yields eigenvectors that are orthogonal to each other. This orthogonality property of those subspaces is used to separate noise from desired isgnal and get a ...

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