The audio spectrogram is a time-frequency representation that has been widely
used for audio classification. One of the key attributes of the audio
spectrogram is the temporal resolution, which depends on the hop size used in
the Short-Time Fourier Transform (STFT). Previous works generally assume the
hop size should be a constant value (e.g., 10 ms). However, a fixed temporal
resolution is not always optimal for different types of sound. The temporal
resolution affects not only classification accuracy but also computational
cost. This paper proposes a novel method, DiffRes, that enables differentiable
temporal resolution modeling for audio classification. Given a spectrogram
calculated with a fixed hop size, DiffRes merges non-essential time frames
while preserving important frames. DiffRes acts as a “drop-in” module between
an audio spectrogram and a classifier and can be jointly optimized with the
classification task. We evaluate DiffRes on five audio classification tasks,
using mel-spectrograms as the acoustic features, followed by off-the-shelf
classifier backbones. Compared with previous methods using the fixed temporal
resolution, the DiffRes-based method can achieve the equivalent or better
classification accuracy with at least 25% computational cost reduction. We
further show that DiffRes can improve classification accuracy by increasing the
temporal resolution of input acoustic features, without adding to the
computational cost.
In this article, the authors discuss the importance of temporal resolution in audio spectrograms and propose a novel method called DiffRes for audio classification. The temporal resolution in audio spectrograms is determined by the hop size used in the Short-Time Fourier Transform (STFT). While previous works have assumed a constant hop size, the authors argue that a fixed temporal resolution may not be optimal for different types of sound.
DiffRes addresses this issue by allowing differentiable temporal resolution modeling for audio classification. It achieves this by merging non-essential time frames in a spectrogram while preserving important frames. DiffRes acts as a module between an audio spectrogram and a classifier, enhancing the temporal resolution and reducing computational costs. Importantly, DiffRes can be jointly optimized with the classification task.
The authors conducted evaluations on five audio classification tasks using mel-spectrograms as acoustic features and off-the-shelf classifier backbones. The results showed that DiffRes-based methods achieved equivalent or better classification accuracy compared to previous methods that used fixed temporal resolution. Furthermore, the DiffRes-based approach achieved at least a 25% reduction in computational cost.
This research has multi-disciplinary implications within the field of multimedia information systems. By improving the temporal resolution of audio classification, it can enhance the accuracy and efficiency of tasks such as speech recognition, music genre classification, and sound event detection. The DiffRes method can also be applied to other areas of multimedia processing like video classification and image recognition, expanding its potential impact.
Moreover, the concepts discussed in this article are closely related to animations, artificial reality, augmented reality, and virtual realities. Audio plays a significant role in creating immersive multimedia experiences. Enhancing the classification of audio in these contexts can lead to more realistic virtual environments, interactive augmented reality applications, and improved audio synchronization in animations. The DiffRes method has the potential to enhance the audio processing capabilities in these areas, enriching the overall user experience.