An audio recognition method, comprising: acquiring a to-be-detected audio signal; determining that an acquisition scenario of the to-be-detected audio signal is a first scenario according to the to-be-detected audio signal; and determining, based on the to-be-detected audio signal by using a sound event recognition model corresponding to the first scenario, a sound event identified by the to-be-detected audio signal, wherein the sound event recognition model corresponding to the first scenario is a neural network model trained by using an audio signal in the first scenario and configured to identify a sound event in the first scenario based on the audio signal, wherein the to-be-detected audio signal comprises a plurality of audio frames; and the determining that an acquisition scenario of the to-be-detected audio signal is a first scenario according to the to-be-detected audio signal comprises: inputting each of the plurality of audio frames into a scenario recognition model, to obtain scenario information of each audio frame, wherein the scenario recognition model is a neural network model trained by using audio frames in a plurality of scenarios and configured to determine an acquisition scenario of an audio frame based on the audio frame, and the scenario information of each audio frame is used for indicating a probability that an acquisition scenario of each audio frame is each of the plurality of scenarios; and determining that the acquisition scenario of the to-be-detected audio signal is the first scenario in the plurality of scenarios according to the scenario information of each audio frame. acquiring a to-be-detected audio signal; determining that an acquisition scenario of the to-be-detected audio signal is a first scenario according to the to-be-detected audio signal; and determining, based on the to-be-detected audio signal by using a sound event recognition model corresponding to the first scenario, a sound event identified by the to-be-detected audio signal, wherein the sound event recognition model corresponding to the first scenario is a neural network model trained by using an audio signal in the first scenario and configured to identify a sound event in the first scenario based on the audio signal, wherein the to-be-detected audio signal comprises a plurality of audio frames; and the determining that an acquisition scenario of the to-be-detected audio signal is a first scenario according to the to-be-detected audio signal comprises: inputting each of the plurality of audio frames into a scenario recognition model, to obtain scenario information of each audio frame, wherein the scenario recognition model is a neural network model trained by using audio frames in a plurality of scenarios and configured to determine an acquisition scenario of an audio frame based on the audio frame, and the scenario information of each audio frame is used for indicating a probability that an acquisition scenario of each audio frame is each of the plurality of scenarios; and determining that the acquisition scenario of the to-be-detected audio signal is the first scenario in the plurality of scenarios according to the scenario information of each audio frame. inputting each of the plurality of audio frames into a scenario recognition model, to obtain scenario information of each audio frame, wherein the scenario recognition model is a neural network model trained by using audio frames in a plurality of scenarios and configured to determine an acquisition scenario of an audio frame based on the audio frame, and the scenario information of each audio frame is used for indicating a probability that an acquisition scenario of each audio frame is each of the plurality of scenarios; and determining that the acquisition scenario of the to-be-detected audio signal is the first scenario in the plurality of scenarios according to the scenario information of each audio frame. <EOS>