An audio signal classification method, comprising: performing frame division processing on an input audio signal; obtaining a linear prediction residual energy tilt of a current audio frame, wherein the linear prediction residual energy tilt denotes an extent to which linear prediction residual energy of the audio signal changes as a linear prediction order increases; storing the linear prediction residual energy tilt in a memory; and classifying the audio frame according to statistics of a part of data of prediction residual energy tilts in the memory; wherein the statistics of the part of the data of the prediction residual energy tilts is a variance of the part of the data of the prediction residual energy tilts; and the classifying the audio frame according to statistics of a part of data of prediction residual energy tilts in the memory comprises: comparing the variance of the part of the data of the prediction residual energy tilts with a music classification threshold, and when the variance of the part of the data of the prediction residual energy tilts is less than the music classification threshold, classifying the current audio frame as a music frame; otherwise classifying the current audio frame as a speech frame; wherein the obtaining a linear prediction residual energy tilt of a current audio frame comprises: obtaining the linear prediction residual energy tilt of the current audio frame according to the following formula: epsP _ tilt = ∑ i = 1 n epsP i ⋅ epsP i + 1 ∑ i = 1 n epsP i ⋅ epsP i, wherein epsP(i) denotes prediction residual energy of i th -order linear prediction of the current audio frame; and n is a positive integer, denotes a linear prediction order, and is less than or equal to a maximum linear prediction order. performing frame division processing on an input audio signal; obtaining a linear prediction residual energy tilt of a current audio frame, wherein the linear prediction residual energy tilt denotes an extent to which linear prediction residual energy of the audio signal changes as a linear prediction order increases; storing the linear prediction residual energy tilt in a memory; and classifying the audio frame according to statistics of a part of data of prediction residual energy tilts in the memory; wherein the statistics of the part of the data of the prediction residual energy tilts is a variance of the part of the data of the prediction residual energy tilts; and the classifying the audio frame according to statistics of a part of data of prediction residual energy tilts in the memory comprises: comparing the variance of the part of the data of the prediction residual energy tilts with a music classification threshold, and when the variance of the part of the data of the prediction residual energy tilts is less than the music classification threshold, classifying the current audio frame as a music frame; otherwise classifying the current audio frame as a speech frame; wherein the obtaining a linear prediction residual energy tilt of a current audio frame comprises: obtaining the linear prediction residual energy tilt of the current audio frame according to the following formula: epsP _ tilt = ∑ i = 1 n epsP i ⋅ epsP i + 1 ∑ i = 1 n epsP i ⋅ epsP i, wherein epsP(i) denotes prediction residual energy of i th -order linear prediction of the current audio frame; and n is a positive integer, denotes a linear prediction order, and is less than or equal to a maximum linear prediction order. comparing the variance of the part of the data of the prediction residual energy tilts with a music classification threshold, and when the variance of the part of the data of the prediction residual energy tilts is less than the music classification threshold, classifying the current audio frame as a music frame; otherwise classifying the current audio frame as a speech frame; wherein the obtaining a linear prediction residual energy tilt of a current audio frame comprises: obtaining the linear prediction residual energy tilt of the current audio frame according to the following formula: epsP _ tilt = ∑ i = 1 n epsP i ⋅ epsP i + 1 ∑ i = 1 n epsP i ⋅ epsP i, wherein epsP(i) denotes prediction residual energy of i th -order linear prediction of the current audio frame; and n is a positive integer, denotes a linear prediction order, and is less than or equal to a maximum linear prediction order. obtaining the linear prediction residual energy tilt of the current audio frame according to the following formula: epsP _ tilt = ∑ i = 1 n epsP i ⋅ epsP i + 1 ∑ i = 1 n epsP i ⋅ epsP i, wherein epsP(i) denotes prediction residual energy of i th -order linear prediction of the current audio frame; and n is a positive integer, denotes a linear prediction order, and is less than or equal to a maximum linear prediction order.