There are many different ways to analyze audio, and each has its benefits and drawbacks. In this post, we’ll be looking at how to build an all-in-one audio analysis toolkit using Python.
This toolkit will allow us to perform a wide variety of tasks, including:
-Converting audio to a text representation
-Identifying the key and tempo of a piece of music
-Extracting pitch and melodic information
-Analyzing the harmonic structure of a piece of music
-Performing sound classification
Building such a toolkit will require us to use several different libraries, including:
-numpy
-scipy
-librosa
-matplotlib
We’ll also need to use some specialized functions from within these libraries. Let’s get started!
Converting audio to a text representation:
The first step in our audio analysis process will be to convert our audio files into a text representation. This will allow us to manipulate and analyze the data more. We’ll be using the librosa library for this task.
Librosa provides several different functions for audio analysis. In this case, we’ll be using the to_mono() function to convert our stereo audio files into a single-channel (mono) representation.
We’ll also be using the resample() function to downsample our audio. This is generally a good idea when working with audio data, as it can help to reduce noise and improve our computational efficiency.
Once we’ve downsampled our audio, we can then use the mfcc() function to extract the Mel-frequency cepstral coefficients. These coefficients provide us with a compact representation of the spectral envelope of our audio signal.
Finally, we’ll use the librosa.feature.delta() function to compute the first-order and second-order derivatives of our MFCCs. These derivatives will allow us to capture additional information about our audio signal.
Identifying the key and tempo of a piece of music:
Now that we have a text representation of our audio data, we can begin to analyze it. One of the first things we might want to do is identify the key and tempo of a piece of music.
We can use the librosa.beat.tempo() function to estimate the tempo of a piece of music. This function returns an estimate of the number of beats per minute.
We can also use the librosa.feature.tempogram() function to compute a tempo histogram. This histogram can be used to visualize the distribution of tempos in a piece of music.
To identify the key of a piece of music, we’ll use the librosa.feature.key() function. This function returns a key estimate for a piece of music. The estimate is based on the mean pitch of the music.
Extracting pitch and melodic information:
Once we’ve identified the key and tempo of a piece of music, we can begin to extract pitch and melodic information.
We can use the librosa.core.pitches() function to extract pitch information from an audio signal. This function returns a numpy array of pitch values.
We can also use the librosa.core.harmonics() function to extract harmonic information from an audio signal. This function returns a numpy array of harmonic values.
Analyzing the harmonic structure of a piece of music:
Once we’ve extracted pitch and melodic information, we can begin to analyze the harmonic structure of a piece of music.
We can use the librosa.core.harmonic_mean() function to compute the harmonic mean of a piece of music. This value can be used to characterize the overall tonality of a piece of music.
We can also use the librosa.core.harmonic_std() function to compute the harmonic standard deviation of a piece of music. This value can be used to quantify the amount of tonal variation in a piece of music.
Performing sound classification:
Once we’ve analyzed the harmonic structure of a piece of music, we can then begin to perform sound classification.
Sound classification is the process of assigning a class label to an audio signal. There are several different ways to perform sound classification, but in this case, we’ll be using the librosa.feature.mfcc() function.
This function computes the MFCCs for an audio signal. We can then use these MFCCs to train a classifier. In this case, we’ll be using a support vector machine (SVM) for our classification.
Once we’ve trained our classifier, we can then use it to predict the class label for new audio signals.
Conclusion:
In this post, we’ve seen how to build an all-in-one audio analysis toolkit using Python. This toolkit can be used to perform a wide variety of tasks, including:
-Converting audio to a text representation
-Identifying the key and tempo of a piece of music
-Extracting pitch and melodic information
-Analyzing the harmonic structure of a piece of music
-Performing sound classification