A gas sensing device for sensing a gas in a mixture of gases, the gas sensing device comprising: a photoacoustic spectrometry device, wherein the photoacoustic spectrometry device comprises a radiator configured for emitting light, wherein the photoacoustic spectrometry device comprises a gas detection chamber configured for exposing the mixture of gases to the light, wherein the photoacoustic spectrometry device comprises a microphone configured for detecting sound in the gas detection chamber, which is caused by exposing the mixture of gases to the light, and wherein the photoacoustic spectrometry device is configured for generating signal samples corresponding to a concentration of the gas in the mixture of gases based on the sound detected by the microphone; and a computing device configured for receiving the signal samples, wherein the computing device comprises a feature extraction block configured for calculating representations for the signal samples so that for each of the signal samples one of the representations is calculated, wherein each of the representations comprises one or more feature values, wherein each of the one or more feature values refer to a characteristic of the respective signal sample; wherein the computing device comprises a decision making block which comprises a trained model based algorithm block having a plurality of inputs and at least one output, wherein the decision making block comprises one or more trained models for the trained model based algorithm block, wherein each of the feature values of one of the representations is input to one of the inputs of the trained model based algorithm block, so that each feature value of the feature values is fed into an individual input of the inputs, wherein the decision making block creates sensing results based on output values of the at least one output of the trained model based algorithm block, wherein the output values are created by using at least one of the one or more trained models at the decision making block so that the output values depend on the signal samples of the photoacoustic spectrometry device; characterized in that the feature extraction block is configured in such way that some of the feature values of the representations correspond to an absolute value of a mean deviation of the respective signal sample or to a phase of a mean deviation of the respective signal sample or to an absolute value of a standard deviation of the respective signal sample or to a phase of a standard deviation of the respective signal sample. a photoacoustic spectrometry device, wherein the photoacoustic spectrometry device comprises a radiator configured for emitting light, wherein the photoacoustic spectrometry device comprises a gas detection chamber configured for exposing the mixture of gases to the light, wherein the photoacoustic spectrometry device comprises a microphone configured for detecting sound in the gas detection chamber, which is caused by exposing the mixture of gases to the light, and wherein the photoacoustic spectrometry device is configured for generating signal samples corresponding to a concentration of the gas in the mixture of gases based on the sound detected by the microphone; and a computing device configured for receiving the signal samples, wherein the computing device comprises a feature extraction block configured for calculating representations for the signal samples so that for each of the signal samples one of the representations is calculated, wherein each of the representations comprises one or more feature values, wherein each of the one or more feature values refer to a characteristic of the respective signal sample; wherein the computing device comprises a decision making block which comprises a trained model based algorithm block having a plurality of inputs and at least one output, wherein the decision making block comprises one or more trained models for the trained model based algorithm block, wherein each of the feature values of one of the representations is input to one of the inputs of the trained model based algorithm block, so that each feature value of the feature values is fed into an individual input of the inputs, wherein the decision making block creates sensing results based on output values of the at least one output of the trained model based algorithm block, wherein the output values are created by using at least one of the one or more trained models at the decision making block so that the output values depend on the signal samples of the photoacoustic spectrometry device; characterized in that the feature extraction block is configured in such way that some of the feature values of the representations correspond to an absolute value of a mean deviation of the respective signal sample or to a phase of a mean deviation of the respective signal sample or to an absolute value of a standard deviation of the respective signal sample or to a phase of a standard deviation of the respective signal sample.