| Signal processing algorithms are a category of algorithms that can be employed for tackling different types of signal issues.These can encompass communication signals,text signals,speech signals,etc.Communication signals are usually digital or analog signals.Text signals can be represented in various ways,including using character codes such as ASCII,or natural language text signals such as English or Chinese.Speech signals are analog signals that are typically digitized and used for applications such as speech recognition and synthesis.Currently,one of the main challenges faced by communication signal processing is the problem of channel distortion,while text signals often encounter difficulties when dealing with large models.Speech recognition faces several challenges,including the need for large model sizes,and the difficulty in obtaining datasets.In this paper,we present potential solutions to the signal problems discussed above and primarily focus on the following tasks:Firstly,a semi-supervised precoding scheme has been proposed to estimate the precoder parameters by analyzing the characteristics of the cross-correlation source signals and the precoders,which simplifies the transmission process and enhances the security of the communication network.In the field of blind separation,the precoding system can be used to improve the separation performance,signal-to-noise ratio of the separated signals,estimation accuracy of the channel matrix,and convergence speed of the algorithm.However,the current precoding system relies excessively on precoding parameters which must be obtained by the receiver in advance.This requirement results in additional bandwidth and transmission resources,and the precoding parameters must remain unchanged during the transmission process.The proposed scheme allows for the random generation of precoding parameters,and the effectiveness of the proposed method has been demonstrated by experimental results.Secondly,The traditional text signal processing concept has been extended to deep learning by incorporating the BERT(Bidirectional Encoder Representation from Transformers)model for pre-training,and a BGRU-CNN model based on BERT and double-layer knowledge distillation has been presented to handle text-based emotion recognition problems.The feature sets of both the “teacher model” and “student model” are calculated,and then gradient descent is performed using mean square error to transfer the knowledge of the “teacher model” soft label probability distribution to the “student model”,with the addition of an attention mechanism.Using classification accuracies on the same dataset as the evaluation metric,the experimental results show that the accuracy of the proposed model is higher compared to singlelayer knowledge distillation learning with “teacher” model labels,with a maximum improvement of 2.72%.Thirdly,in order to tackle the issue of large speech recognition models and difficulty in acquiring sufficient training data for speech signal processing,a small sample speech recognition model has been proposed by utilizing knowledge distillation and mutual learning.On one hand,the model has been condensed through knowledge distillation as mentioned above.At the same time,the accuracy of the compressed model is improved by utilizing a mutual learning framework.On the other hand,the teacher model has been augmented with the BERT model to compensate for the limited training data.Multiple experiments have been conducted on the TIMIT dataset,and the proposed model has achieved an acceptable word error rate while utilizing less data.In summary,innovative solutions have been provided to address current challenges in the field of signal processing.It is expected to have a positive impact on promoting further research and application of signal processing techniques. |
