# Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection
###### Abstract  
Determining whether an audio signal is single compressed (SC) or double compressed (DC) is a crucial task in audio forensics, as it is closely linked to the integrity of the recording. In this paper, we propose the utilization of phase spectrum-based features for detecting DC narrowband and wideband adaptive multi-rate (AMR-NB and AMR-WB) speech. To the best of our knowledge, phase spectrum features have not been previously explored for DC audio detection. In addition to introducing phase spectrum features, we propose a novel parallel LSTM system that simultaneously learns the most representative features from both the magnitude and phase spectrum of the speech signal and integrates both sets of information to further enhance its performance. Analyses demonstrate significant differences between the phase spectra of SC and DC speech signals, suggesting their potential as representative features for DC AMR speech detection. The proposed phase spectrum features are found to perform as well as magnitude spectrum features for the AMR-NB codec, while outperforming the magnitude spectrum in detecting AMR-WB speech. The proposed phase spectrum features yield 8% performance improvement in terms of true positive rate over the magnitude spectrogram features. The proposed parallel LSTM system further improves DC AMR-WB speech detection. DC AMR speech detection; AMR-NB; AMR-WB; audio forensics +
Footnote †: journal: Applied Sciences  
code [4; 5] stands out in several respects. Firstly, while the majority of encoders are perceptual codecs designed to compress audio signals (often music signals), based on the characteristics of the human ear, the AMR codec is primarily tailored for speech signals. Secondly, AMR speech coding has emerged as a mandatory codec for GSM services, making it the most prevalent speech codec in use. Thirdly, unlike other perceptual audio codecs, the AMR codec employs the code excited linear prediction (CELP) model instead of considering the masking effect on the human ear. Lastly, most smartphones and audio recorders save recordings in AMR format, and numerous freely available audio editing software tools can convert AMR-compressed signals into PCM waveform or other formats. Consequently, individuals lacking prior experience in digital signal processing, such as forgers or fraudsters, can easily modify AMR signals. As a result, this study focuses on detecting double-compressed (DC) AMR speech signals. The magnitude of the discrete Fourier transform (DFT) of short speech segments, commonly known as the spectrogram representation of speech signals, has found widespread application in various tasks such as speaker recognition [6], speech recognition [7], emotion recognition [8], and audio event detection [9]. Its effectiveness stems from its ability to capture the spectral content variation of the signal over time, making it suitable for use with deep neural networks (DNNs), such as deep convolutional neural networks (CNNs), and long-short-term memory (LSTM) networks. In our previous work, we introduced the use of spectrograms for the first time in detecting narrowband (sampling frequency of 8 kHz) DC AMR (AMR-NB) speech signals [10], achieving state-of-the-art performance. However, the spectrogram representation only utilizes the magnitude spectrum, discarding the phase information of the Fourier transform. Numerous studies in the literature have highlighted the utility of the phase spectrum in tasks such as synthetic/converted speech detection [11; 12], speech recognition [13], and speaker recognition [14]. Additionally, research has emphasized the perceptual importance of phase information in speech coding [15; 16] and human speech perception [17]. Previous studies have indicated that preserving the original phase spectrum results in more natural sounding reconstructed speech, underscoring the potential value of phase spectrum features in distinguishing SC and DC speech signals.