# An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition
## 1 Introduction  
In recent years, while research in auditory recognition has often been focusing on automatic speech recognition (ASR), music classification [1], and acoustic scene classification (ASC), the environmental event sound recognition (ESC) problem has also received increasing attention from the research community with popular applications in audio surveillance systems [2] and noise mitigation [3]. In the ESC problem or sound event detection problem, the goal is to recognize the event type of a specific sound, such as a dog bark, car horn, or engine. These sound events include various daily audio events with chaotic and diverse structure [4] and can be categorized into three groups: single sounds such as a mouse-click, repeated discrete sounds such as clapping hands or typing on a keyboard, and steady continuous sounds such as the sound of a vacuum cleaner or engine [5]. The sound event classification problem is sometimes confused with the related ASC problem, in which an input sound is required to be classified into different acoustic scenes. Each acoustic scene may contain a collection of sound events involved in the surrounding environment. For example, a 'bus' scene may be identified from frequently occurring sound events such as acceleration, braking, passenger announcements, and door opening sounds, while the engine and other people's conversations exist in the background [6]. Here, we limit the scope of our study to the recognition of specific or individual sound events in an environment. A variety of traditional audio features such as zero-crossing, mel frequency cepstral coefficients [7; 8; 9], and wavelet transformation have been used to represent sound features for an environmental sound event. Traditional machine learning algorithms such as K nearest neighbor, support vector machines, and Gaussian mixture models have been applied to classify the sound [10; 11; 12; 13]. With the popularity of deep learning, deep neural networks [14; 15; 16; 17] have also been proposed to solve the sound event recognition problem in an environment. In particular, the convolutional neural network is regarded as being most suitable for this problem [18; 19], due to its capability to capture time and frequency features when applied to spectrogram-like inputs. Three different ways of applying CNNs to sound event recognition have been proposed. The first approach is to use the CNN as the classifier with log-mel features as the input [4; 19], which are extracted from environmental sound. We refer to this method as logmel-CNN. The second CNN approach learns to classify the sound directly from raw waveforms without feature engineering. These raw-CNN methods use CNNs to extract audio features from raw wave signals for ESC [20; 21; 22; 23]. [PERSON] [5] proposed an end-to-end system (EnvNet) to classify raw waveform signals using two convolution layers. The accuracy of their method was 5.1% higher than that of the models using static log-mel features. The third hybrid CNN approach combines raw-CNN and logmel-CNN methods for this task [5] using an average fusion method and achieved a better performance than that of either method alone. Current approaches have the following limitations: (1) since the log-mel feature was originally designed for ASR rather than for ESC tasks, it may fail to capture some information of the audio events that may be critical for further improved performance; (2) currently, end-to-end recognition models with feature learning cannot surpass CNN models with static log-mel and delta log-mel features in terms of recognition accuracy. This indicates that new neural network models are needed to achieve high-performance ESC; (3) the current average fusion method can reflect overall prediction results of the hybrid models, but has the drawback of neglecting the tendency of individual models. It can also be easily affected by extreme values, leading to recognition errors.