The invention belongs to the technical field of audio quality analysis, and discloses an
algorithm-based audio quality
analysis method and
system, and the method comprises the steps: employing a lightweight CRNN model to fuse audio-video dual-mode features through the cooperation of a multi-problem joint detection module and a self-adaptive
feature fusion extraction module, and carrying out the
algorithm-based audio quality analysis. The five problems of abnormal volume,
noise and the like are synchronously detected in combination with a multi-
task learning framework, and a bottom-layer feature network is shared to avoid parameter conflicts; meanwhile, weighted binary
cross entropy loss is introduced, and the
detection rate of low-probability problems such as howling and sound interruption is enhanced; compared with a traditional sub-module scheme, the design eliminates the contradiction that
noise suppression excessively weakens voice, the multi-problem detection accuracy is remarkably improved, the result consistency is greatly improved, and the accurate detection requirement for
concurrency of multiple problems in a conference scene is met; and full-
link adaptation is realized through a dynamic parameter adjustment mechanism: the difference
perception feature learning expands the normal / abnormal frame feature difference, and the sensitivity of a low
signal-to-
noise ratio scene is improved.