HPR4638: Simple Podcasting - Episode 3 - Analyzing and Filtering

This show has been flagged as Clean by the host. 01 This is the third in a four part series on simple podcasting. 02 In this episode we will cover the following topics: Analysis of audio noise problems and filtering methods used to deal with specific problems that we may find. Command line recording. Command line playback. Getting information about an audio recording. 03 Introduction When I did my first couple of podcasts I didn't notice that there was a quiet high pitched whine or buzz in the background. Nobody complained about it, but I thought I could do better in subsequent episodes. 04 Creating an Audio Sample If you have a similar problem, the first step is to find out where it is coming from. If there is no audible noise where you are recording, there is a good chance the problem is in the microphone or another part of the audio system. Plug in your microphone and record 2 or 3 seconds of quiet audio where you do not speak into the microphone or make other noise. 05 You will need a minimum amount of data in order to analyze it. For a flac file sampled at 44.1 kHz, 2 to 3 seconds of data should be enough. To get a sample of just electronic noise you can put the microphone in a drawer or somewhere like that if you want to be sure of getting a quiet signal. Any sound recorded in this way should be mainly from the microphone or other electronic elements in the analogue pathway. To get a sample of possible ambient noise, such as fans, make sure the microphone is in the open air in an area which is representative of where it will be when you are recording. -------------------- 06 Analyzing using Fourier Transforms Next you need to look at the wave form. At this point I will describe this using Audacity. I will show other ways later, but Audacity is actually the easiest if you are starting from nothing. You don't need to become an expert in Audacity to use it, just follow the steps I will describe. I myself don't know how to use Audacity beyond using this one feature. 07 We are going to analyze the sound spectrum in our sample. The technique being used is a Fourier Transform. A Fourier transform, often called an "FFT" for fast fourier transform, is a mathematical method of showing a signal in terms of frequency along the x axis instead of time. This allows us to spot troublesome noise frequencies which appear when we don't want them to. The FFT is a very common mathematical technique which is widely used in signal processing, not just in audio. 08 There is software which will create pretty coloured animations of sound waves, but this is not what you want. These are simply decorative patterns and won't tell us what we want to know. -------------------- 09 Using Audacity Install Audacity if you haven't already. Start Audacity. Select file > import > audio, then navigate to your sample and select "open". The file should load. 10 In the wave form part of the window, click anywhere and then type Ctrl-S to select all data points. The chart should turn a slightly darker colour. From the menu, select Analyze > Plot Spectrum. A new window will open, showing magnitude in db on the Y axis, and frequency in hertz on the x axis. For "algorithm" be sure it is set to "spectrum" 11 There are now two settings that we need to play with while we look for problems. One is "size" The default for this is 1024. The other is "axis". The default for this is "log frequency". -------------------- 12 What to Look For What we are looking for are large obvious spikes that stand out in the data. Since our test signal has very little to no actual audio data, any spikes should represent electrical or other noise that doesn't belong there. 13 I have found two combinations of settings to be most helpful in finding problems. These are Size 2048, axis linear frequency. Size 32768, axis log frequency. 14 A small size value can help very narrow spikes stand out from the background more, while a large size value can help separate spikes from surrounding noise. A linear frequency axis can help with seeing all spikes across the f