A Survey of Application of Machine Learning Algorithms in Signal Recognition

Signal recognition is vital for sectors like medical diagnosis, security monitoring, intelligent transportation, and voice interaction. Traditional methods, however, relying on manually designed features, struggle with complex patterns and high-dimensional signals. While machine learningespecially deep learningaddresses these issues via end-to-end learning and automatic feature representation, it suffers from over-reliance on high-quality labeled data. Real-world challenges such as signal noise and high labeling costs lead to label noise, impeding practical application. This review explores signal recognition fundamentals, including feature extraction, selection and classification. It details deep learning applications in image recognition, like CNNs, RNNs and Transformers. It also discusses multimodal learning, using AV-ASR and BPO-AVASR as examples. Finally, it identifies challenges (data scarcity, high model complexity, privacy issues) and proposes future directions (lightweight models, few-shot learning). The review concludes that deep learning dominates signal recognition, with models achieving human-level performance on benchmarks. Multimodal learning, fusing speech and image data, is a key trend.