# Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection
## 2 Related Work  
Considering existing studies, research on DC audio signal detection can be divided into three categories based on the audio codec used: (i) DC MP3 audio detection, (ii) DC AAC audio detection, and (iii) DC AMR speech detection. The majority of early studies focused on DC audio detection employed the MP3 codec, with the aim of distinguishing whether an MP3 audio file belongs to the SC or DC class. Similarly, interest in detecting DC AAC-encoded audio, another widely used perceptual audio codec found in platforms like Apple iTunes, YouTube, and DVD standards, grew after numerous studies were conducted on the AAC codec. DC AMR speech detection, on the other hand, has gained considerable attention in recent years due to its extensive usage in smartphones and digital voice recorders. In this section, we provide a brief overview of the existing literature for each category of studies. In [18], it was demonstrated that high-frequency components (above 16 kHz) were attenuated when a CD-quality music signal was compressed using an MP3 codec with a low bit rate (128 kbps). However, it was observed that these high-frequency components became perceptible when a higher bit rate was used. Consequently, the authors proposed employing a support vector machine (SVM) classifier with spectral features extracted from the 16 kHz to 20 kHz band of the audio spectrogram to detect the original compression bit rate of the MP3 audio signals. Detection of \"fake-quality\" MP3 recordings, where an MP3 recording compressed at a low bit rate is decoded and re-encoded at a higher bit rate, using the number of small valued modified discrete cosine transform (MDCT) coefficients, was addressed in [19]. Similarly, in [20], statistical features extracted from zero-valued and nonzero-valued MDCT coefficients were used with SVM classifiers to detect DC MP3 audio signals. The use of statistical features derived from quantized MDCT (QMDCT) coefficients for DC MP3 audio detection was proposed in [21; 22]. In [23], the distribution of the nine most significant digits of the MDCT coefficients was employed to detect MP3 double compression using an SVM classifier. In [24; 25], authors computed the Chi-square distance between the histograms of observed and simulated MDCT coefficients and then applied a threshold to the distance value to determine whether the MP3 audio is SC or DC. In [26], the authors applied the Markov model to the Huffman codebook indices to extract features and utilized an SVM classifier for detecting DC AAC signals. It was reported that the proposed features gave higher performance when the first compression bit rate (BR1) was lower than that of the second compression bit rate (BR2). In [27], QMDCT coefficients were used with the SVM classifier to detect DC AAC signals. It was found that QMDCT coefficients provided reasonable performance when the BR2 value was higher than BR1. In [28], it was demonstrated that the audio scale factor decreases as the number of compression iterations increases, making it suitable for detecting DC AAC signals. A detection rate above 99% was achieved using the proposed features with the SVM classifier. In [29], authors proposed using QMDCT coefficient features with a CNN classifier to detect the compression bit rate of SC AAC audio signals. From the brief literature review on MP3 and AAC compression detection presented above, it is evident that features computed from MDCT coefficients were widely utilized, with reported detection rates exceeding 95% in most studies. The remarkable performance of MDCT-based features in detecting MP3 or AAC compression is unsurprising, considering that both MP3 and AAC codecs utilize MDCT coefficients for encoding audio signals. Therefore, extracting features with prior knowledge about the encoder is expected to yield high performance without a doubt. In the initial study addressing DC AMR-NB speech signal detection, statistical features based on spectral energy distribution and correlation of spectral components were proposed and used with the SVM classifier [30].