Anomaly detection for the compressor systems is essential for the midstream industry. In this paper, anomaly classification and detections method based on neural network hybrid model named as Long Short-Term Memory (LSTM)-Autoencoder (AE) is proposed to detect anomalies on sequence pattern of audio data, collected by multiple sound sensors deployed at different components of each compressor system. To create a baseline for model evaluation this paper has also conducted experiments on different RNN architectures such as GRU, LSTM, Stacked LSTM and Stacked GRU with different functions. Each architecture used audio signals dataset received from the compressor system for experiments to consider the performance in each neural network model. All the network architectures and experiment results have considered with various model configuration and layers with different functions. According to performance results, optimal model for anomaly detection with best performance scores has proposed in this research. In conducted experiments, combined one-dimensional raw audio signals features using SC and Mel spectrogram features were fed to deep learning models to evaluate performance. Using SC and Mel spectrogram features achieved the best performance for anomaly detection in audio data based on the LSTM-AE in all evaluation metrics. Hence such hybrid methods can detect normal and anomaly audio signals collected from a compressor system effectively, which can increase the compressor reliability and the gas production line sustainability.