#3817 Librosa Feature Extraction

-

#3817 Librosa Feature Extraction

#3817 Provide a python application using librosa to read an MP3 to produce arrays of 8 distinct musical properties

Using the librosa library in Python, you can load an MP3 file and extract various musical properties (features). Since librosa requires external libraries like FFmpeg or GStreamer for MP3 support via its dependency audioread, ensure one of these is installed on your system.

Here is a Python application that reads an MP3 file and extracts 8 distinct musical properties as NumPy arrays:

🎶 Python Librosa Feature Extraction

import librosa
import librosa.feature
import numpy as np

def extract_musical_features(file_path):
    """
    Reads an MP3 file and extracts 8 distinct musical properties using librosa.

    Args:
        file_path (str): The path to the MP3 file.

    Returns:
        dict: A dictionary where keys are the feature names and values are 
              the resulting NumPy arrays or scalar values.
    """
    try:
        # 1. Load the audio file (y: audio time series, sr: sampling rate)
        # librosa.load handles MP3 via audioread dependency (requires ffmpeg/gstreamer)
        y, sr = librosa.load(file_path, sr=22050, mono=True)
        
        # Initialize dictionary to hold features
        features = {}

        # --- Extract 8 Distinct Features ---
        
        # 2. Mel-Frequency Cepstral Coefficients (MFCCs) - Represents spectral shape
        # Typically used for recognition/classification. We'll take the array.
        features['mfcc'] = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
        
        # 3. Zero-Crossing Rate (ZCR) - Measures the rate of sign changes in the signal
        # Good for characterizing percussive vs. harmonic content.
        features['zero_crossing_rate'] = librosa.feature.zero_crossing_rate(y)

        # 4. Spectral Centroid - Indicates the 'center of mass' of the spectrum
        # Correlates with the perceived 'brightness' of the sound.
        features['spectral_centroid'] = librosa.feature.spectral_centroid(y=y, sr=sr)[0]

        # 5. Spectral Bandwidth - Measures the spread of the spectrum around its centroid.
        features['spectral_bandwidth'] = librosa.feature.spectral_bandwidth(y=y, sr=sr)[0]

        # 6. Spectral Roll-off - The frequency below which a specified percentage of the 
        # total spectral energy lies (e.g., 85%).
        features['spectral_rolloff'] = librosa.feature.spectral_rolloff(y=y, sr=sr)[0]
        
        # 7. Chromagram (Chroma Short-Time Fourier Transform) - Measures the 
        # intensity of the 12 pitch classes (C, C#, D, etc.). Useful for harmony/tonality.
        features['chroma_stft'] = librosa.feature.chroma_stft(y=y, sr=sr)
        
        # 8. Root Mean Square (RMS) Energy - Measures the magnitude of the signal, 
        # often related to loudness.
        features['rms_energy'] = librosa.feature.rms(y=y)[0]
        
        # 9. Estimated Tempo (BPM) - Measures the tempo in beats per minute.
        # This returns a scalar (float), not a frame-wise array like others, but it's a distinct musical property.
        features['tempo'], _ = librosa.beat.beat_track(y=y, sr=sr)
        
        return features

    except Exception as e:
        print(f"An error occurred: {e}")
        print("Make sure you have all dependencies installed (e.g., 'pip install librosa' and either FFmpeg or GStreamer for MP3 support).")
        return None

# --- Example Usage ---
# NOTE: Replace 'your_audio_file.mp3' with the actual path to your MP3 file.
mp3_file = 'your_audio_file.mp3' 
extracted_data = extract_musical_features(mp3_file)

if extracted_data:
    print("\n✅ Extracted Musical Features:")
    for feature_name, array in extracted_data.items():
        if isinstance(array, np.ndarray):
            print(f"- **{feature_name}**: NumPy Array, Shape: {array.shape}, Data Type: {array.dtype}")
        else:
             print(f"- **{feature_name}**: Scalar Value, Value: {array}")

The video Audio processing in Python with Feature Extraction for machine learning provides a tutorial on using librosa for audio processing and feature extraction, including some of the features mentioned above.

Would you like me to elaborate on what any of these specific musical properties represent in more detail?


In addition to the 8 features, what other data can be extracted from an mp3?

That's a great question! Besides the 8 low-level acoustic features already extracted, you can derive several other types of valuable data from an MP3 using librosa and related Python packages. These additional data points often represent high-level musical structure or time-frequency analysis.

Here are some other key types of data you can extract from an MP3:

📊 Core Signal Data

This is the raw or primary transformed signal data, which all other features are typically derived from.

  • Waveform (y and sr):

    • The fundamental data extracted by librosa.load().

    • Data: A 1-dimensional NumPy array (1y) of amplitude values over time, and the sampling rate (2sr) in Hertz.3

    • Use: This is the time-domain representation of the audio.4

  • Short-Time Fourier Transform (STFT):

    • Data: A complex-valued 2D array representing the spectrogram (time vs. frequency). The magnitude of this array, 5|S|, is the standard spectrogram.6

    • Use: Shows how the frequency content of the signal changes over time. It's the basis for most spectral features.

  • Mel Spectrogram:

    • Data: A 2D array similar to the STFT spectrogram but with frequencies warped onto the Mel scale, which is perceptually uniform.7

    • Use: Closer to how the human ear perceives pitch, it's often more effective than a standard spectrogram for machine learning tasks.

🎵 Structural and Temporal Data

These features analyze the music's structure over time, giving insight into rhythm and tonality.

FeatureLibrosa FunctionDescription
Beat Frames/Timeslibrosa.beat.beat_track()The indices (frames) or timestamps (seconds) where musical beats occur. Essential for synchronization.
Onset Timeslibrosa.onset.onset_detect()The precise times when notes or percussive events begin. Crucial for rhythm analysis.
Harmonic/Percussive Separationlibrosa.effects.hpss()Separates the audio into two new time-series arrays: one containing the harmonic (tonal) components and one containing the percussive (rhythmic) components.
Tonal Centroid Features (Tonnetz)librosa.feature.tonnetz()A 6-dimensional representation of musical pitch that is invariant to octaves. Used for advanced harmonic analysis.

⚙️ Feature Manipulation Data

These functions don't extract new information but provide derivatives of existing arrays, which are very useful for analysis.

  • Delta Features:

    • Data: A new array representing the local estimate of the derivative (change) of an existing feature (like MFCCs).

    • Use: Captures the velocity or rate of change of a feature over time, which can be critical in music and speech recognition. You can also calculate the second derivative (delta-delta).

  • Feature Statistics:

    • Data: Single scalar values (mean, standard deviation, median, min, max, etc.) calculated across the entire array for any time-series feature (e.g., the average Spectral Centroid for the whole song).

    • Use: Reduces large time-series arrays into a small, fixed-size feature vector, often required as input for machine learning models.

Would you like to see a code snippet demonstrating how to perform Harmonic/Percussive Separation or extract Beat Times?


List the full set of librosa.features

The librosa.feature module contains a comprehensive suite of functions for extracting low-level musical and acoustic features from audio signals. These features are generally categorized as Spectral, Rhythm, Manipulation, and Inversion.

Here is the full set of functions available within librosa.feature (based on librosa version 0.11.0 documentation):

✨ Complete librosa.feature Function Set

Spectral Features

These functions analyze the frequency content of the audio signal.

Feature FunctionDescription
librosa.feature.chroma_stftCompute a standard Chromagram (pitch classes C, C#, D... over time).
librosa.feature.chroma_cqtCompute a Chromagram using the Constant-Q Transform (CQT) for better resolution in lower frequencies.
librosa.feature.chroma_censCompute the Chroma Energy Normalized Statistics (CENS), which smooths local deviations for better musical key recognition.
librosa.feature.chroma_vqtCompute a Chromagram using the Variable-Q Transform (VQT).
librosa.feature.melspectrogramCompute a Mel-scaled spectrogram.
librosa.feature.mfccCompute Mel-Frequency Cepstral Coefficients (MFCCs).
librosa.feature.rmsCompute the Root Mean Square (RMS) energy for each frame (often used for loudness).
librosa.feature.spectral_centroidCompute the spectral centroid (perceived brightness).
librosa.feature.spectral_bandwidthCompute the spectral bandwidth (spread of energy around the centroid).
librosa.feature.spectral_contrastCompute spectral contrast (difference between peaks and valleys in the spectrum).
librosa.feature.spectral_flatnessCompute spectral flatness (measure of how "noise-like" the spectrum is).
librosa.feature.spectral_rolloffCompute the spectral roll-off frequency (frequency below which a certain percentage of energy lies).
librosa.feature.poly_featuresGet coefficients of fitting an nth-order polynomial to the columns of a spectrogram.
librosa.feature.tonnetzCompute the Tonal Centroid Features (Tonnetz), a 6-dimensional representation of harmony.
librosa.feature.zero_crossing_rateCompute the Zero-Crossing Rate (ZCR) (rate of sign changes in the signal).

Rhythm Features

These functions are used for analyzing the rhythmic properties of the audio.

Feature FunctionDescription
librosa.feature.tempoEstimate the tempo (Beats Per Minute, BPM).
librosa.feature.tempogramCompute the tempogram: local autocorrelation of the onset strength envelope.
librosa.feature.fourier_tempogramCompute the Fourier tempogram: the short-time Fourier transform of the onset strength envelope.
librosa.feature.tempogram_ratioCompute the tempogram ratio features (also known as spectral rhythm patterns).

Feature Manipulation

These functions are utilities for processing features after they've been extracted.

Feature FunctionDescription
librosa.feature.deltaCompute delta features: the local estimate of the first, second, etc., derivative of the input data.
librosa.feature.stack_memoryCreate a short-term history embedding by vertically concatenating delayed copies of the data matrix (useful for sequential modeling).

Feature Inversion

These functions are used to reverse the feature extraction process to approximate the original audio.1

Feature FunctionDescription
librosa.feature.inverse.mel_to_stftApproximate STFT magnitude from a Mel power spectrogram.
librosa.feature.inverse.mel_to_audioInvert a Mel power spectrogram back to audio using the Griffin-Lim algorithm.
librosa.feature.inverse.mfcc_to_melInvert MFCCs to approximate a Mel power spectrogram.
librosa.feature.inverse.mfcc_to_audioConvert MFCCs to a time-domain audio signal.

The documentation for all these feature extraction methods can be found in this resource on audio processing with librosa: Audio processing in Python with Feature Extraction for machine learning.


From <https://gemini.google.com/app/>  Google Gemini 2.5 Pro

Comments

Post a Comment

Popular posts from this blog

#2892 Example of ChatGPT o1 Prompt

-
#2892 Example of ChatGPT o1 Prompt #2892 Consider this prompt example from a tutorial:  """The Anatomy of an o1 Prompt Goal: I want a list of the best medium-length hikes within two hours of San Francisco. Each hike should provide an unique adventure, and be lesser know. Return Format: For each hike, return the name of the hike as I'd find it on AllTrails, then provide the starting address of the hike, the ending address of the hike, distance, drive time, hike duration, and what makes it a cool and unique adventure. Warnings: Be careful to make sure the name of the trail is correct, that it actually exists, and that the time is correct! Context Dump: For context: my girlfriend and I hike a ton! We've don pretty much all of the local SF hikes, whether that's Presidio or Golden Gate Park. we definitely want to get out of town -- we did mount tam pretty recently, the whole thing from the beginning of the stairs to Stinson -- it was really long and we are def...
Read more
-
#65 Compose a song about mankind traveling to the stars to find life, riches and happiness Verse 1 : We set out on a journey to the stars Searching for a new world, free from scars A place where we can find our destiny And build a life that's meant to be Chorus : We are the explorers, the brave and true On a mission to find what's waiting for us In the vast expanse of the universe We'll go where no man has gone before Verse 2 : We'll cross the galaxy, we'll go far and wide To find a home where we can thrive We'll brave the dangers that come our way And find the riches that lay hidden in the stars Chorus : We are the explorers, the brave and true On a mission to find what's waiting for us In the vast expanse of the universe We'll go where no man has gone before Bridge : We'll meet new friends and see new sights And learn about the mysteries of the night We'll find new worlds and new life And see what the future holds in our sights Chorus : We are ...
Read more

#3105 Federal and State Spending Idiocy

-
#3105 Federal and State Spending Idiocy A historic highway overpass for animals cost nearly $100 million and is almost done https://www.yahoo.com/news/historic-highway-overpass-animals-cost-100115264.html #3105 Considering that a state is building a 100-foot wide grass covered overpass across 6 lanes of interstate and shoulders, describe all budget items and the total budget for this task. Reasoned about overpass construction budgeting for a few seconds I'm mapping out a budget for a 100-foot wide grass-covered overpass. Categories include design, permitting, site preparation, structural construction, landscaping, signage, safety features, environmental mitigation, traffic management, and contingency, totaling an approximate $20-30 million. Below is a sample, high-level breakdown of typical budget items for constructing a 100-foot-wide, grass-covered overpass across six interstate lanes (plus shoulders). Actual costs vary significantly depending on location, terrain, materials, lab...
Read more