Environmental Sound Classification

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 This project Recognize sounds (e.g., rain, dog bark, traffic) to categorize environmental audio. The UrbanSound8K dataset offers diverse labeled environmental sound clips, making it straightforward to set up and test. This Project is also available on PaperWithCode . 

Environmental Sound Classification (ESC) is a crucial aspect of acoustic signal processing, enabling the automatic detection and categorization of various sound events frequently heard indoors or outdoors. ESC involves analyzing complex audio signals using features based on time and frequency domains. This field is essential for applications like hearing aids, crime investigation, and security systems. Recent advancements include the use of deep learning models, such as 1D Convolutional Neural Networks (CNNs), which learn representations directly from audio signals. These models capture fine time structures and learn diverse filters relevant to the classification task. ESC continues to evolve, with ongoing research focusing on improving accuracy and efficiency.


UrbanSound8K

This dataset contains 8732 labeled sound excerpts (<=4s) of urban sounds from 10 classes:

  1. air_conditioner,
  2. car_horn,
  3. children_playing,
  4. dog_bark,
  5. drilling,
  6. enginge_idling,
  7. gun_shot,
  8. jackhammer,
  9. siren and
  10. street_music


Best Performing Model in ESC

One of the top-performing models in Environmental Sound Classification (ESC) is AudioCLIP. This model extends the CLIP (Contrastive Language-Image Pretraining) framework to include audio, achieving state-of-the-art results in ESC tasks. AudioCLIP combines image, text, and audio data to learn robust representations, reaching accuracies of 90.07% on the UrbanSound8K dataset and 97.15% on the ESC-50 dataset. The model leverages deep convolutional neural networks (CNNs) and data augmentation techniques to enhance performance.


Ancient Techniques in Sound Classification

Before the advent of deep learning and machine learning, sound classification relied on more traditional signal processing techniques. These methods included:

  1. Fourier Transform: Used to convert time-domain signals into frequency-domain representations. This helped in analyzing the frequency components of sounds.

  2. Mel-Frequency Cepstral Coefficients (MFCCs): A feature extraction technique that represents the short-term power spectrum of a sound. MFCCs were widely used in speech and sound recognition.

  3. Zero-Crossing Rate (ZCR): Measures the rate at which the signal changes sign. It was used to detect the presence of high-frequency content in sounds.

  4. Spectrogram Analysis: Visual representations of the spectrum of frequencies in a sound signal as they vary with time. Spectrograms were used to manually analyze and classify sounds.


ESC in the Past

In the past, ESC was primarily done using manual analysis and simpler algorithms. Researchers and engineers would manually extract features from audio signals using the techniques mentioned above. These features were then fed into traditional classifiers like:

  • Gaussian Mixture Models (GMMs): Used to model the probability distribution of features.

  • Hidden Markov Models (HMMs): Used for sequential data and time-series analysis.

  • Support Vector Machines (SVMs): Used for classification tasks by finding the optimal hyperplane that separates different classes.

These methods required significant domain expertise and manual effort to design and extract relevant features from audio signals. The accuracy and efficiency of these techniques were limited compared to modern deep learning approaches.

ESC has come a long way from manual feature extraction and traditional classifiers to sophisticated deep learning models like AudioCLIP, which leverage large datasets and powerful neural networks to achieve high accuracy and robustness in sound classification tasks.

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