Again, the long-term average spectra of the program prove to be a good indicator of the program's effectiveness as a test signal. [PERSON] [9; 10] used music samples containing different temporal and tonal content in the subjective evaluation of room modes below 120 Hz. It is found that the content of music stimuli has a significant effect on the detection of modal problems. The music sample with fast-paced notes had trouble revealing perceptual differences between different room control methods [10]. [PERSON] attributed this result to the differences in temporal and musical characteristics between the two samples. He also stressed the importance of establishing a proper selection strategy of test samples to extract meaningful and reliable results. Other research [15] regarding the audibility of resonances at low frequencies also provides evidence that the temporal structure of the signal is important for hearing peaks and dips at low frequencies. These results suggest that the features of programs are related to the program's ability to discriminate between different devices in the listening test. In this respect, by selecting programs with suitable features, the perceptual difference of the interested attributes between different loudspeakers under test could be excited. Moreover, a prediction model based on the features may be built to assess if a program is suitable for the listening test. This approach has already been applied in the field of speech quality evaluation where different models were built based on the audio and video features to enhance speech from a noisy environment [16; 17]. Compared with noise signals or some specific synthetic signals, musical signals are more preferred in many listening tests to simulate real-world situations. In recent years, a large number of audio features have been extracted to characterize musical signals from different aspects [18; 19; 20]. In this work, by using various audio features of the music programs, the program selection criteria for those listening tests regarding the low-frequency evaluation of loudspeakers are investigated. The main research questions are summarized as follows. (1) Which program materials are better at revealing the spectral differences in the low-frequency range? (2) What are the common features of these programs? (3) Is it possible to build a model based on the audio features to predict if the program is suitable for the listening test? The work methodology was divided into two parts. In the first part, a discrimination test was conducted to evaluate the ability of the programs to reveal the spectral differences in the low-frequency range. Based on the discrimination test results, cluster analysis was performed to separate the programs into two groups: one group contains the more discriminating and revealing programs while the other group is composed of programs with poor performance. In the second part, a discriminant analysis was used to explain and predict the membership of programs to the two groups based on various characteristics of the programs, including dynamic, timbal, rhythmic, and spectral features. The result of the discriminant analysis was validated using the cross-validation method. The paper is organized as follows: Section 2 describes the listening test used to evaluate the discrimination ability of the programs. Section 3 presents the listening test results and performs cluster analysis on the programs. Section 4 analyzes the relationship between the revealing ability and the characteristics of the programs using discriminant analysis, followed by discussion and conclusions in Sections 5 and 6 respectively.