For audio search tasks, three neural network architectures are widely used: convolutional neural networks (CNNs), transformer-based models, and embedding-based architectures like Siamese or triplet networks. These approaches excel at extracting meaningful patterns from audio data, enabling efficient similarity matching or retrieval.
CNNs are a natural fit for audio search because they process spectrograms—visual representations of audio frequencies over time—as 2D inputs. By applying convolutional layers, CNNs learn local patterns (e.g., harmonics, temporal changes) that generalize well across audio clips. For example, models like VGGish (adapted from VGG for audio) or customized CNNs pre-trained on large datasets like AudioSet are often used to generate audio embeddings. These embeddings can then be compared using cosine similarity or other metrics for search. CNNs are computationally efficient and work well for tasks like music recommendation or sound effect retrieval, where spectral features matter more than long-term temporal context.
Transformer-based models have gained traction for handling sequential audio data, especially when long-range dependencies are critical. By using self-attention mechanisms, transformers capture relationships across time steps in raw waveforms or mel-spectrograms. Models like Wav2Vec 2.0 or the Audio Spectrogram Transformer (AST) are pretrained on massive audio datasets, allowing them to learn rich representations. For speech-centric search (e.g., finding a spoken phrase), transformers outperform CNNs in capturing nuanced temporal patterns. They are also adaptable to multimodal tasks, such as aligning audio with text or video. However, transformers require more computational resources, making them less ideal for real-time applications without optimization.
Embedding-based architectures like Siamese or triplet networks focus on learning a similarity space for audio clips. These models train on pairs or triplets of audio samples (e.g., anchor, positive, negative) to minimize the distance between similar clips and maximize it for dissimilar ones. For instance, a triplet network using contrastive loss can generate embeddings where two versions of the same song are closer than unrelated tracks. This approach is particularly effective for voice-based search or fingerprinting (e.g., Shazam-like systems). Tools like TensorFlow Similarity or PyTorch Metric Learning simplify implementation. While these models depend on high-quality training data, they offer flexibility in integrating with other architectures, such as combining CNN feature extractors with triplet loss layers.
In practice, hybrid approaches are common. For example, a CNN might extract initial features, followed by a transformer for temporal modeling, and finally a triplet loss for embedding refinement. The choice depends on factors like dataset size, latency requirements, and whether the audio is speech, music, or environmental sounds. Libraries like Librosa for preprocessing and frameworks like PyTorch or TensorFlow provide the building blocks to experiment with these architectures.