Re: [music-dsp] resampling
Hi Alex, This is a good read: https://ccrma.stanford.edu/~jos/resample/ Using Google, I found somebody who used the LGPL code available at Julius' site: https://github.com/intervigilium/libresample Good luck! Esteban On 7/19/2018 2:15 PM, Alex Dashevski wrote: Hi, I need to convert 48Khz to 8KHz on input and convert 8Khz to 48Khz on audio on output. Could you explain how to do it ? I need to implement this on android(NDK). Thanks, Alex ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp -- Esteban Maestre Computational Acoustic Modeling Lab Department of Music Research, McGill University http://ccrma.stanford.edu/~esteban ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
Re: [music-dsp] parametric string synthesis
Nice demos! In http://ieeexplore.ieee.org/document/7849104/ we point to a multi-modal string quartet multi-modal (audio, contact mics, mocap, video, etc) dataset we recorded some time ago. I believe it's also listed in the MTG-UPF website. As for your excitation signal, perhaps some temporary "chaos" in your oscillator synchronization method might help with the attacks. Cheers, Esteban On 3/14/2018 1:45 PM, gm wrote: I made a little demo for parametric string synthesis I am working on: https://soundcloud.com/traumlos_kalt/parametric-strings-test/s-VeiPk It's a morphing oscillator made from basic "virtual analog" oscillator components (with oscillator synch) to mimic the bow & string "Helmholtz" waveform, fed into a simplified body filter. The body is from a cello and morphed in size for viola, cello and bass timbres (I know that's not accurate). It's made from a very sparse stereo FIR filter (32 taps). It doesn't sound like the real instrument body response, but the effect still sounds somewhat physcial to me. The idea is to replace the VA "Helmholtz" oscillator with a wavetable oscillator (with synch?) which is controlled by paramterized playing styles, to be more flexible and more natural behaving than sample libraries. And a better body filter. The advantage over waveguide modeling with a bow model would be that you don't have to play the bow with accurate pressure and velocity, and that it is more cpu friendly and more flexible in regards to more artificial timbres and timbre morphing. So far it's a private hobby project in Reaktor 5, but it maybe has some potential I believe. Doesn't sound like samples yet but maybe it will when the model is improved... At least it can provide an instrument with a hybrid sound between virtual analog and physical which is something I love to use in my music. I used the body filter with synths quite often. So far the "Helmholtz" waveform is made from assumptions like that that it behaves like a synched oscillator depending on the ratio between the two sides of the string, which might not be true. Why I am posting this: Maybe someone here plays an electric solid body violin or something similar and can provide samples of bow & string waveforms with different playing styles and notes for analysis? And has an interest to join efforts to create this instrument? Or maybe someone even knows of a source for such waveforms? ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp -- Esteban Maestre Computational Acoustic Modeling Lab Department of Music Research, McGill University http://ccrma.stanford.edu/~esteban ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
Re: [music-dsp] tracking drum partials
https://ccrma.stanford.edu/~scottl/thesis.html Esteban On 7/27/2017 4:02 PM, Thomas Rehaag wrote: @Esteban: Have you got a link to a (Levine and Smith, 98) PDF? Found the other one and it looks promising after a short glimpse. -- Esteban Maestre Computational Acoustic Modeling Lab Department of Music Research, McGill University http://ccrma.stanford.edu/~esteban ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
Re: [music-dsp] tracking drum partials
Hi Thomas, Sinusoidal-only modeling could be limiting for some of the intrinsic features of percussive sounds. A possibility would be to encode partials + noise (Serra and Smith, 89) or partials + noise + transients (Levine and Smith, 98). Cheers, Esteban On 7/26/2017 4:37 PM, Thomas Rehaag wrote: Dear DSP Experts, can anybody tell me how to track drum partials? Is it even possible? What I'd like to have are the frequency & amplitude envelopes of the partials so that I can rebuild the drum sounds with additive synthesis. I've tried it with heavily overlapping FFTs and then building tracks from the peaks. Feeding the results into the synthesis (~60 generators) brought halfway acceptable sounds. Of course after playing with FFT- and overlapping step sizes for a while. But those envelopes were strange and it was very frustrating to see the results when I analyzed a synthesized sound containing some simple sine sweeps this way. Got a good result for the loudest sweep. But the rest was scattered in short signals with strange frequencies. Large FFTs have got the resolution to separate the partials but a bad resolution in time so you don't even see the higher partials which are gone within a short part of the buffer. With small FFTs every bin is crowded with some partials. And every kind of mask adds the more artifacts the smaller the FFT is. Also tried BP filter banks. Even worse! It's always resolution in time and frequency fighting each other too much for this subject. Any solution for this? Best Regards, Thomas ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp -- Esteban Maestre Computational Acoustic Modeling Lab Department of Music Research, McGill University http://ccrma.stanford.edu/~esteban ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
Re: [music-dsp] Music applications/DSP online masters
Hi Liam, I believe https://ccrma.stanford.edu/academics/masters is an excellent program. It's not an online program, but at least it happens close to where you live. At Kadenze.com you'll find online (related) courses. Esteban On 7/20/2016 3:11 PM, Liam Sargent wrote: Hello all, Been subscribed to this list for a while and have found the conversation fascinating. I recently graduated with a B.S. in Computer Science and have a strong interest in continuing my education in DSP programming for audio applications. I have recently started a full time job in the SF Bay Area as a software engineer - will likely have to complete course material online. Wondering anyone on this list has recommendations for a solid online M.S. program focused on audio signal processing/music applications, or just resources for continuing my learning in general. Liam ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp -- Esteban Maestre CIRMMT/CAML - McGill Univ MTG - Univ Pompeu Fabra http://ccrma.stanford.edu/~esteban ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
Re: [music-dsp] Cheap spectral centroid recipe
Hi there, On 2/25/2016 3:57 PM, Evan Balster wrote: When working with tonal signals, it has been proposed that brightness be normalized through division by fundamental frequency. This produces a dimensionless (?) metric which is orthogonal to the tone's pitch, and does not typically fall below a value of one. Whether such a metric corresponds more closely to brightness than the spectral centroid in hertz depends on a psychoacoustics question: Do humans perceive brightness as a quality which is independent from pitch? Interesting topic. Finding a (more-or-less) universal numerical recipe that can be used to predict a perceptual, verbally designated attribute (in this case "brightness") represents in itself a difficult problem with many potential biases. An example is the definition of "brightness", which might be subject to language-specific and tone- / instrument- specific biases. Regarding the methods proposed in this thread, I personally believe that an audio frame could be split into deterministic (partials) and stochastic (noise floor) components (see Xavier Serra's work from 1989), and propose different "centroid" measures for each of these components, which could then be combined in some desired way. In any case, many researchers have studied the /orthogonality/ between perceived brightness and fundamental frequency in certain contexts. This is an example: http://newt.phys.unsw.edu.au/~jw/reprints/SchubertWolfe06.pdf But if I had to give a name, I would probably go for Stephen McAdams. Cheers, Esteban -- Esteban Maestre CIRMMT/CAML - McGill Univ MTG - Univ Pompeu Fabra http://ccrma.stanford.edu/~esteban ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
Re: [music-dsp] Fourier and its negative exponent
By the way: complex-conjugate does not mean it rotates in opposite direction; check out this picture: http://www.eetasia.com/STATIC/ARTICLE_IMAGES/200902/EEOL_2009FEB04_DSP_RFD_NT_01c.gif Rotation in opposite direction happens with negative frequencies. Cheers, Esteban On 10/5/2015 8:06 PM, Stijn Frishert wrote: Thanks Allen, Esteban and Sebastian. My main thought error was thinking that negating the exponent was the complex equivalent of flipping the sign of a non-complex sinusoid (sin and -sin). Of course it isn’t. e^-a isn’t the same as -e^a. The real part of a complex sinusoid and its complex conjugate are the same, they only rotate in different directions. And so the minus is to negate that rotation in the complex plane. Correct me if I’m wrong, of course. Stijn On 5 Oct 2015, at 15:51, Allen Downey <dow...@allendowney.com <mailto:dow...@allendowney.com>> wrote: In Chapter 7 of Think DSP, I develop the DFT in a way that might help with this: http://greenteapress.com/thinkdsp/html/thinkdsp008.html If you think of the inverse DFT as matrix multiplication where the matrix, M, contains complex exponentials as basis vectors, the (forward) DFT is the multiplication by the inverse of M. Since M is unitary, its inverse is its conjugate transpose. The conjugation is the source of the negative sign, when you write the DFT in summation form. Allen On Mon, Oct 5, 2015 at 9:28 AM, Stijn Frishert <stijnfrish...@gmail.com <mailto:stijnfrish...@gmail.com>> wrote: Hey all, In trying to get to grips with the discrete Fourier transform, I have a question about the minus sign in the exponent of the complex sinusoids you correlate with doing the transform. The inverse transform doesn’t contain this negation and a quick search on the internet tells me Fourier analysis and synthesis work as long as one of the formulas contains that minus and the other one doesn’t. So: why? If the bins in the resulting spectrum represent how much of a sinusoid was present in the original signal (cross-correlation), I would expect synthesis to use these exact same sinusoids to get back to the original signal. Instead it uses their inverse! How can the resulting signal not be 180 phase shifted? This may be text-book dsp theory, but I’ve looked and searched and everywhere seems to skip over it as if it’s self-evident. Stijn Frishert ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu <mailto:music-dsp@music.columbia.edu> https://lists.columbia.edu/mailman/listinfo/music-dsp ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu <mailto:music-dsp@music.columbia.edu> https://lists.columbia.edu/mailman/listinfo/music-dsp ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp -- Esteban Maestre CIRMMT/CAML - McGill Univ MTG - Univ Pompeu Fabra http://ccrma.stanford.edu/~esteban ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
Re: [music-dsp] Fourier and its negative exponent
"does not mean" > "does mean" Esteban On 10/5/2015 8:47 PM, Esteban Maestre wrote: By the way: complex-conjugate does not mean it rotates in opposite direction; check out this picture: http://www.eetasia.com/STATIC/ARTICLE_IMAGES/200902/EEOL_2009FEB04_DSP_RFD_NT_01c.gif Rotation in opposite direction happens with negative frequencies. Cheers, Esteban On 10/5/2015 8:06 PM, Stijn Frishert wrote: Thanks Allen, Esteban and Sebastian. My main thought error was thinking that negating the exponent was the complex equivalent of flipping the sign of a non-complex sinusoid (sin and -sin). Of course it isn’t. e^-a isn’t the same as -e^a. The real part of a complex sinusoid and its complex conjugate are the same, they only rotate in different directions. And so the minus is to negate that rotation in the complex plane. Correct me if I’m wrong, of course. Stijn On 5 Oct 2015, at 15:51, Allen Downey <dow...@allendowney.com> wrote: In Chapter 7 of Think DSP, I develop the DFT in a way that might help with this: http://greenteapress.com/thinkdsp/html/thinkdsp008.html If you think of the inverse DFT as matrix multiplication where the matrix, M, contains complex exponentials as basis vectors, the (forward) DFT is the multiplication by the inverse of M. Since M is unitary, its inverse is its conjugate transpose. The conjugation is the source of the negative sign, when you write the DFT in summation form. Allen On Mon, Oct 5, 2015 at 9:28 AM, Stijn Frishert <stijnfrish...@gmail.com> wrote: Hey all, In trying to get to grips with the discrete Fourier transform, I have a question about the minus sign in the exponent of the complex sinusoids you correlate with doing the transform. The inverse transform doesn’t contain this negation and a quick search on the internet tells me Fourier analysis and synthesis work as long as one of the formulas contains that minus and the other one doesn’t. So: why? If the bins in the resulting spectrum represent how much of a sinusoid was present in the original signal (cross-correlation), I would expect synthesis to use these exact same sinusoids to get back to the original signal. Instead it uses their inverse! How can the resulting signal not be 180 phase shifted? This may be text-book dsp theory, but I’ve looked and searched and everywhere seems to skip over it as if it’s self-evident. Stijn Frishert ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu <mailto:music-dsp@music.columbia.edu> https://lists.columbia.edu/mailman/listinfo/music-dsp ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu <mailto:music-dsp@music.columbia.edu> https://lists.columbia.edu/mailman/listinfo/music-dsp ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp -- Esteban Maestre CIRMMT/CAML - McGill Univ MTG - Univ Pompeu Fabra http://ccrma.stanford.edu/~esteban -- Esteban Maestre CIRMMT/CAML - McGill Univ MTG - Univ Pompeu Fabra http://ccrma.stanford.edu/~esteban ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
Re: [music-dsp] Fourier and its negative exponent
HI Stijn, That "minus" comes from complex-conjugate (of Euler's formula). To find the projection coefficients (Fourier Transform), in each of the terms in the summation one computes the inner product of two complex vectors: the complex sinusoid you are "testing", and its complex-conjugate. The resulting complex number (each bin is a complex number) will not only tell you "how much of a sinusoid was present in the original signal", but also its relative phase. This is an excellent read: https://ccrma.stanford.edu/~jos/st/ Cheers, Esteban On 10/5/2015 4:28 PM, Stijn Frishert wrote: Hey all, In trying to get to grips with the discrete Fourier transform, I have a question about the minus sign in the exponent of the complex sinusoids you correlate with doing the transform. The inverse transform doesn’t contain this negation and a quick search on the internet tells me Fourier analysis and synthesis work as long as one of the formulas contains that minus and the other one doesn’t. So: why? If the bins in the resulting spectrum represent how much of a sinusoid was present in the original signal (cross-correlation), I would expect synthesis to use these exact same sinusoids to get back to the original signal. Instead it uses their inverse! How can the resulting signal not be 180 phase shifted? This may be text-book dsp theory, but I’ve looked and searched and everywhere seems to skip over it as if it’s self-evident. Stijn Frishert ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp -- Esteban Maestre CIRMMT/CAML - McGill Univ MTG - Univ Pompeu Fabra http://ccrma.stanford.edu/~esteban ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
Re: [music-dsp] Fourier and its negative exponent
On 10/5/2015 6:15 PM, Esteban Maestre wrote: the complex sinusoid you are "testing", and its complex-conjugate Sorry: I mean "your signal and the complex sinusoid your are testing". Esteban -- Esteban Maestre CIRMMT/CAML - McGill Univ MTG - Univ Pompeu Fabra http://ccrma.stanford.edu/~esteban ___ dupswapdrop: music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
Re: [music-dsp] Compensate for interpolation high frequency signal loss
On 8/18/2015 6:41 AM, Jerry wrote: I would think that polynomial interpolators of order 30 or 40 would provide no end of unpleasant surprises due to the behavior of high-order polynomials. I'm thinking of weird spikes, etc. Have you actually used polynomial interpolators of this order? I remember going even above 40-th order with no problems. But I also remember having problems with 80-th order interpolation. I think it's called the /Runge phenomenon/. Esteban -- Esteban Maestre CIRMMT/CAML - McGill Univ MTG - Univ Pompeu Fabra http://ccrma.stanford.edu/~esteban ___ music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
Re: [music-dsp] Compensate for interpolation high frequency signal loss
No experience with compensation filters here. But if you can afford to use a higher order interpolation scheme, I'd go for that. Using Newton's Backward Difference Formula, one can construct time-varying, table-free, efficient Lagrange interpolation schemes of arbitrary order (up to 30-th or 40-th order) which stay within linear complexity while allowing for run-time modulation of the interpolation order. https://ccrma.stanford.edu/~jos/Interpolation/Lagrange_Interpolation.html Cheers, Esteban On 8/17/2015 12:07 PM, STEFFAN DIEDRICHSEN wrote: I could write a few lines over the topic as well, since I made such a compensation filter about 17 years ago. So, there are people, that do care about that topic, but there are only some, that do find time to write up something. ;-) Steffan On 17.08.2015|KW34, at 17:50, Theo Verelst theo...@theover.org mailto:theo...@theover.org wrote: However, no one here besides RBJ and a few brave souls seems to even care much about real subjects. ___ music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp -- Esteban Maestre CIRMMT/CAML - McGill Univ MTG - Univ Pompeu Fabra http://ccrma.stanford.edu/~esteban ___ music-dsp mailing list music-dsp@music.columbia.edu https://lists.columbia.edu/mailman/listinfo/music-dsp
Re: [music-dsp] Calculating e^(i*phase) * (H + H')
Hi MF, It is difficult for me to understand your question or the context of your question. I also don't get the derivative part. One question: are H and H' complex conjugate, and respectively defined for positive and negative frequencies? It would be helpful to know what you are trying to do, or at least which paper are you trying to learn from. Cheers, Esteban On 4/1/2015 4:07 PM, MF wrote: Hi Forum, I am trying to implement a formula from a paper: Y(w) = e^(i*phase) * (H(w) + H’(w)) Where H is the fourier transform of a window function h (a blackman window in my case), H’ is the derivative of H (in the paper, H and H' are called spectrum motifs). A signal will then be generated from ifft(Y). In the paper it says: In practice, the signals to be synthesized are real, and the inverse FFT algorithm only uses the positive frequency half spectrum, *so only one of the two spectral motifs must be synthesized*. I don’t understand what it means by “only one of the two spectral motifs must be synthesized”. How do I decide which spectral motive to use? ps. I simplified the formula for simplicity. But in case you want to see the complete formula: Y(wk) = e^(i*phase) * (0.5 * A * H(wk - wf) + 0.5* B *H’(wk - wf)) for |wk - wf| = K * 2pi / N ps2. sorry I just by accident posted the same question without a subject an hour ago, is there a way to delete it from the archive? Thanks in advance!!! MF -- dupswapdrop -- the music-dsp mailing list and website: subscription info, FAQ, source code archive, list archive, book reviews, dsp links http://music.columbia.edu/cmc/music-dsp http://music.columbia.edu/mailman/listinfo/music-dsp -- Esteban Maestre CIRMMT/CAML - McGill Univ MTG - Univ Pompeu Fabra http://ccrma.stanford.edu/~esteban -- dupswapdrop -- the music-dsp mailing list and website: subscription info, FAQ, source code archive, list archive, book reviews, dsp links http://music.columbia.edu/cmc/music-dsp http://music.columbia.edu/mailman/listinfo/music-dsp
Re: [music-dsp] oversampled Fourier Transform
Hi MF, You might be misreading the frequency-domain versions of your windows. If you think about it, computing windows of different lengths (your oversampled windows) could be seen as obtaining windows of the same duration, but sampled /at different sample rates/. This will lead you to getting frequency-domain signals that are frequency-sampled at different frequency resolutions, and therefore your should be interpreting your frequency-domain samples differently. Let me clarify: Let's assume that you compute three different windows, w1(n), w2(n), and w3(n). All three windows last 1 second. w1(n) was computed by calling blackman(10). Equivalently, w2(n) was computed by calling blackman(20), and w3(n) by calling blackman(40). You can see this as having three windows at different sample rates of 10Hz, 20Hz, and 40Hz. Now if we take the FFT, we obtain W1[k], W2[k], and W3[k], with k being your frequency-domain sample (or bin). The number of frequency-domain samples (bins) of W1[k] is 10, while for W2[k] we have 20 bins, and for W3[k] we have 40 bins. From the sampling theory, we know that - the first 5 bins of W1[k] correspond to frequencies ranging from 0Hz to 5Hz (i.e., half your original sample rate); - the first 10 bins of W2[k] correspond to frequencies ranging from 0Hz to 10Hz (idem as above); - the first 20 bins of W3[k] correspond to frequencies ranging from 0Hz to 20Hz (idem as above). This means that the first 5 bins of all three W1[k], W2[k], and W3[k] will correspond to frequencies ranging from 0Hz to 5Hz. Bingo! Now you can see that all three time-domain windows, for which the only difference was the sample rate at which you obtained them, present /similar/ behavior//in the frequency domain. What we have learned here is that bin-to-bin magnitude (or phase) comparisons will only make sense if the bins correspond to the same frequency, and in this case they do : ) By oversampling the time-domain signal, the only thing you are doing is to add bins in the higher frequency region. In our example, W2[k] has more information than W1[k], but this new information only appears in the frequency region above 5Hz, which was /unknown/ to w1(n) in the first place. Now, going back to your objective. To obtain higher resolution in the lower frequency region, one could take W1[k] and directly resample or oversample it. This could be done by interpolating bins of W1[k]. Interpolation can be accomplished by convolving with a sinc function, which, in the time domain can be seen as multiplying by a rectangular function. Zero-padding is about changing the width of such rectangular function. As we all have done in the past : ), maybe you could read a bit on Windowing, Short-Time Fourier Transform, Zero-Padding, and Sinc Interpolation. It will all fall in place ; ) I totally recommend https://www.createspace.com/3177793 https://ccrma.stanford.edu/~jos/mdft/ , but there are many many other good materials out there! Cheers, Esteban On 3/31/2015 7:10 PM, robert bristow-johnson wrote: On 3/31/15 6:53 PM, Justin Salamon wrote: To expand on what Ethan wrote, it sounds like what you're trying to do is zero-pad the signal: http://www.dsprelated.com/dspbooks/mdft/Zero_Padding.html That said, whilst zero padding will give you an interpolated spectrum in the frequency domain, you may still miss the true location of your peaks, how so? the only mechanism for missing the true location would be sidelobe interference from adjacent peaks, possibly including the peak(s) in negative frequency. and it will also increase the computational cost. Another thing to look at is re-estimating the exact location of the peaks in your spectrum using parabolic interpolation: http://www.dsprelated.com/dspbooks/sasp/Quadratic_Interpolation_Spectral_Peaks.html quadratic interpolation of peaks (given the three discrete samples around the peak) is a good ol' standby. i use it for autocorrelation to get the period to a fractional-sample precision. but i don't see why it would be more accurate than zero-padding before the FFT. Or, you could use the phase instead to compute the instantaneous frequency: http://en.wikipedia.org/wiki/Instantaneous_phase#Instantaneous_frequency needs a Hilbert transformer. and you would also need to isolate the sinusoidal partial from the other partials. i think so, anyway. -- Esteban Maestre CIRMMT/CAML - McGill Univ MTG - Univ Pompeu Fabra http://ccrma.stanford.edu/~esteban -- dupswapdrop -- the music-dsp mailing list and website: subscription info, FAQ, source code archive, list archive, book reviews, dsp links http://music.columbia.edu/cmc/music-dsp http://music.columbia.edu/mailman/listinfo/music-dsp
