Efficient Audio Feature Extraction For Iot Devices Using Low-Power Vlsi Architecture

Abstract

As the use of IoT technology has rapidly advanced, proper algorithms for audio feature extraction that IoT devices can use to process audio data have become critical for the technology’s continued progress. This paper reviews the existing developments in IoT audio feature extraction methods with a view of integrating low-power VLSI systems to boost the performance of the feature extraction methods. Since most IoT devices depend on voice recognition and environmental monitoring, there has been a constant search for high-quality real-time audio processing.

Many trends have been outlined in the course of the study which may define the future development of the audio feature extraction process and one of the most important trends is the use of modern machine learning methods, including deep learning and neural networks. Such approaches also enable devices to learn information features from undifferentiated data and enhance probability measures in recognizing intricate sound patterns. Finally, the paper focuses on shouldering around audio processing in edge computing since it can relieve the strain of latency and bandwidth usage by processing audio data on the edge rather than at the central hub. Housing data processing closer to the source it is being collected from is particularly suitable for requests requiring immediate response times such as voice-activated systems as well as real-time watching over huge streams of data.

Another area of interest in this study is the application of context-aware feature extraction techniques where the amount of noise selected is proportional to the environment’s context. This capability extends user experience and is effective in ensuring that the target device delivers an appropriate response to intended auditory messages. Furthermore, the integration of multimodal data sets where input from the microphone is merged with visual or motion sensor data is proposed as a way of getting a better understanding of complex surroundings.

The work also contextualizes low-power VLSI design in enhancing feature extraction from audio signals for IoT devices with constrained energy sources. The nature of dedicated architectures that are installed solely with audio processing really plays an important role in ensuring kept-up performances yet energy is considered. Last but not least, the paper underlines the fact that most audio processing techniques should incorporate standards and interfaces to enable the transportation of audio data across different IoT devices.

In conclusion, the findings of this study establish audio feature extraction as a path-breaking technology in IoT and identify machine learning, edge computing, context awareness, and low-power design as core vectors for future progress in the field.