Automatic sound recognition (ASR) is a prominently emerging research area in recent years Recognition of sound events automatically through the computers in the complex audio environment is quite useful for machine hearing, acoustic surveillance and multimedia retrieval applications. Although a lot of features such as mel-frequency cepstral coefficients in ASR tasks provide very good results in noiseless environments, noisy conditions in the real world reduce success rates in a remarkable way. On the other hand, it was reported that spectrogram image features showed much better classification performance at low signal noise ratio values in many studies. In this article, it was proposed the preparation of feature vector after the images are reduced in size by applying the resizing process to spectrogram images with Lanczos kernel. Classification performance was compared by using deep artificial neural networks in different noise levels and although the feature vector was reduced, parallel values with results in the literature were obtained in the noiseless environment. It has remained slightly below the current state-of-the-art techniques using spectrogram features while better results compared to other commonly used features such as MFCC were obtained under the noisy conditions. (C) 2017 The Authors. Published by Elsevier B.V.