Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation

1. Model architecture The fully convolutional temporal audio separation network (convt-tasnet) consists of three processing stages, as shown in (A): encoder, separation and decoder. First, an encoder module is used to convert short segments of the hybrid waveform to their corresponding representations in the intermediate feature space. This representation is then used to estimate the […]

Statistical method for assessing the magnitude of driver head posture changes

Face recognition Article directory face recognition Preface 1. Commonly used statistical methods 2. Specific implementation Summarize Foreword Statistical methods to assess the magnitude of driver head posture changes can help analyze driver attention and alertness. 1. Commonly used statistical methods Statistical methods to assess the magnitude of driver head posture changes can help analyze driver […]

Question A: In-depth explanation of earthquake source attribute identification model construction and magnitude prediction with complete code attached – specific modeling process and source code

Question A: Construction of earthquake source attribute identification model and magnitude prediction Problem background: Earthquake is a relatively complex crustal movement phenomenon, and countless earthquake disasters occur around the world every year. Earthquake early warning and forecasting technology aimed at reducing earthquake disasters needs to effectively identify natural earthquake events in daily earthquake monitoring, eliminate […]

Question A: Construction of earthquake source attribute identification model and magnitude prediction: code analysis:

Question 1: For the seismic wave data in appendices 1 to 8, find a series of suitable index Standards and criteria, build a seismic source attribute identification model, and carry out natural earthquake events (Appendix 1-7) Accurate distinction from unnatural seismic events (Annex 8); Question one: import numpy as np import matplotlib.pyplot as plt import […]

pruning+distillation realizes lightweight processing of YOLOv5. Based on three different parameter magnitude models of n/m/s, a tea bud detection model is developed and constructed to explore and analyze the impact of different pruning levels on model performance.

In my many previous blog posts, there are relatively few records about lightweight model post-processing, and most of them are project development. In fact, many previous projects have used corresponding lightweight technologies such as pruning and distillation, but they have not been used separately. For the record, I just have free time this week, so […]

Based on the YOLOv5n/s/m model with different parameter magnitudes, the tea bud detection and recognition model was developed, and the pruning pruning technology was used to lighten the model, and the performance impact of the model under different pruning levels was explored.

Today, I have some time to think about a problem left over from before. I just took it and took a look. The main purpose is to prune the trained target detection model. Here we take the tea bud detection data scene as an example. I have already introduced related practices in my previous blog […]