Amplitude modulation and the sampling theorem

Allen Downey December 18, 20156 comments

I am working on the 11th and probably final chapter of Think DSP, which follows material my colleague Siddhartan Govindasamy developed for a class at Olin College.  He introduces amplitude modulation as a clever way to sneak up on the Nyquist–Shannon sampling theorem.

Most of the code for the chapter is done: you can check it out in this IPython notebook.  I haven't written the text yet, but I'll outline it here, and paste in the key figures.

Convolution...


60 numbers

Mahadevan Srinivasan November 30, 20152 comments

This blog title is inspired from the Peabody award-winning Radiolab episode 60 words. Radiolab is well known for its insightful stories on Science with an amazing sound design. Today's blog is about decoding Radiolab's theme music (actually, just a small "Mmm Newewe" part of it hereafter called the Radiolab sound). I have been taking this online course on Audio Signal Processing where we are taught how to analyze sounds...


Multilayer Perceptrons and Event Classification with data from CODEC using Scilab and Weka

David E Norwood November 25, 2015

For my first blog, I thought I would introduce the reader to Scilab [1] and Weka [2].  In order to illustrate how they work, I will put together a script in Scilab that will sample using the microphone and CODEC on your PC and save the waveform as a CSV file.  Then, we can take the CSV file and open it in Weka.  Once in Weka, we have a lot of paths to consider in order to classify it.  I use the term classify loosely since there are many things you can do with data sets...


Python scipy.signal IIR Filtering: An Example

Christopher Felton May 19, 2013
Introduction

In the last posts I reviewed how to use the Python scipy.signal package to design digital infinite impulse response (IIR) filters, specifically, using the iirdesign function (IIR design I and IIR design II ).  In this post I am going to conclude the IIR filter design review with an example.

Previous posts:


Beat Notes: An Interesting Observation

Rick Lyons March 13, 20137 comments

Some weeks ago a friend of mine, a long time radio engineer as well as a piano player, called and asked me,

"When I travel in a DC-9 aircraft, and I sit back near the engines, I hear this fairly loud unpleasant whump whump whump whump sound. The frequency of that sound is, maybe, two cycles per second. I think that sound is a beat frequency because the DC-9's engines are turning at a slightly different number of revolutions per second. My question is, what sort of mechanism in the airplane...


ICASSP 2011 conference lectures online (for free)

Sami Aldalahmeh July 5, 2011

For the first time, the oral presentations of the International Conference on Accoustics, Speech, and Signal Processing (ICASSP) were recorded and posted online for free. This conference is the best in signal processing and it's diverse as well.

It has a bit speech processing, communication signal processing, and some interesting stuff like bio-inspired signal processing, where Prof. Sayed modeled the behaviour of a group of predetors attacking a herd of preys using distributed least mean...


Fitting Filters to Measured Amplitude Response Data Using invfreqz in Matlab

Julius Orion Smith III October 11, 20102 comments

This blog post has been moved to the code snippet section and can now be found HERE.  Please update your bookmark.  Thanks!


Music/Audio Signal Processing

Julius Orion Smith III September 5, 20087 comments

Greetings,

This is my blog from the point of view of a music/audio DSP research engineer / educator. It is informal and largely nontechnical because nearly everything I have to say about signal processing is (or will be) somewhere in my four-book series: Mathematics of DFT with Audio Applications, Introduction to Digital Filters, Physical Audio Signal Processing and


Components in Audio recognition - Part 1

Prabindh Sundareson November 20, 20076 comments

Audio recognition is defined as the task of recognizing a particular piece of audio (could be music, ring-tone, and speech as well), from a given sample set of audio tracks.

The Human Auditory System (HAS) is unique in that the tasks of "familiarisation" of unknown tracks, and finding "similar" tracks come naturally to us. Tunes from the not-so-recent past can still haunt the human brain many years later, when triggered by a similar tune. The way the brain stores and...


Through the tube...

Markus Nentwig September 15, 20073 comments

Hello all,

something completely different...

there was some recent discussion on the forum about modeling guitar amplifiers.I have been wondering for quite a while, whether the methods that I use to model radio frequency power amplifiers might also work for audio applications.

It's been a rainy day, so I found the time and energy for some experiments. Just for fun.

The device-under-test is a preamplifier with a single 12AX7 tube:

My good ol' Kurzweil (not in the picture) serves as "signal...


Multilayer Perceptrons and Event Classification with data from CODEC using Scilab and Weka

David E Norwood November 25, 2015

For my first blog, I thought I would introduce the reader to Scilab [1] and Weka [2].  In order to illustrate how they work, I will put together a script in Scilab that will sample using the microphone and CODEC on your PC and save the waveform as a CSV file.  Then, we can take the CSV file and open it in Weka.  Once in Weka, we have a lot of paths to consider in order to classify it.  I use the term classify loosely since there are many things you can do with data sets...


Fitting Filters to Measured Amplitude Response Data Using invfreqz in Matlab

Julius Orion Smith III October 11, 20102 comments

This blog post has been moved to the code snippet section and can now be found HERE.  Please update your bookmark.  Thanks!


Through the tube...

Markus Nentwig September 15, 20073 comments

Hello all,

something completely different...

there was some recent discussion on the forum about modeling guitar amplifiers.I have been wondering for quite a while, whether the methods that I use to model radio frequency power amplifiers might also work for audio applications.

It's been a rainy day, so I found the time and energy for some experiments. Just for fun.

The device-under-test is a preamplifier with a single 12AX7 tube:

My good ol' Kurzweil (not in the picture) serves as "signal...


Components in Audio recognition - Part 1

Prabindh Sundareson November 20, 20076 comments

Audio recognition is defined as the task of recognizing a particular piece of audio (could be music, ring-tone, and speech as well), from a given sample set of audio tracks.

The Human Auditory System (HAS) is unique in that the tasks of "familiarisation" of unknown tracks, and finding "similar" tracks come naturally to us. Tunes from the not-so-recent past can still haunt the human brain many years later, when triggered by a similar tune. The way the brain stores and...


The Phase Vocoder Transform

Christian Yost February 12, 2019
1 Introduction

I would like to look at the phase vocoder in a fairly ``abstract'' way today. The purpose of this is to discuss a method for measuring the quality of various phase vocoder algorithms, and building off a proposed measure used in [2]. There will be a bit of time spent in the domain of continuous mathematics, thus defining a phase vocoder function or map rather than an algorithm. We will be using geometric visualizations when possible while pointing out certain group theory...


ICASSP 2011 conference lectures online (for free)

Sami Aldalahmeh July 5, 2011

For the first time, the oral presentations of the International Conference on Accoustics, Speech, and Signal Processing (ICASSP) were recorded and posted online for free. This conference is the best in signal processing and it's diverse as well.

It has a bit speech processing, communication signal processing, and some interesting stuff like bio-inspired signal processing, where Prof. Sayed modeled the behaviour of a group of predetors attacking a herd of preys using distributed least mean...


60 numbers

Mahadevan Srinivasan November 30, 20152 comments

This blog title is inspired from the Peabody award-winning Radiolab episode 60 words. Radiolab is well known for its insightful stories on Science with an amazing sound design. Today's blog is about decoding Radiolab's theme music (actually, just a small "Mmm Newewe" part of it hereafter called the Radiolab sound). I have been taking this online course on Audio Signal Processing where we are taught how to analyze sounds...


A Markov View of the Phase Vocoder Part 1

Christian Yost January 8, 2019
Introduction

Hello! This is my first post on dsprelated.com. I have a blog that I run on my website, http://www.christianyostdsp.com. In order to engage with the larger DSP community, I'd like to occasionally post my more engineering heavy writing here and get your thoughts.

Today we will look at the phase vocoder from a different angle by bringing some probability into the discussion. This is the first part in a short series. Future posts will expand further upon the ideas...


A Free DSP Laboratory

Stephen Morris December 18, 2019
Getting Started In Audio DSP

Imagine you're starting out studying DSP and your particular interest is audio. Wouldn't it be nice to have access to some audio signals and the tools to analyze and modify them? In the old days, a laboratory like this would most likely have cost a lot of time and money to set up. Nowadays, it doesn't have to be like this. The magic of open source software makes it quite straightforward to build yourself a simple audio DSP laboratory – just use the brilliant...


A Markov View of the Phase Vocoder Part 2

Christian Yost January 8, 2019
Introduction

Last post we motivated the idea of viewing the classic phase vocoder as a Markov process. This was due to the fact that the input signal’s features are unknown to the computer, and the phase advancement for the next synthesis frame is entirely dependent on the phase advancement of the current frame. We will dive a bit deeper into this idea, and flesh out some details which we left untouched last week. This includes the effect our discrete Fourier transform has on the...