Proceedings of CMMR 2013, Marseille, France

Proc. of the 10th International Symposium on Computer Music Multidisciplinary Research, Marseille, France, October 15-18, 2013 How Much Does Audio Quality Influence Ratings of Overall Listening Experience? Michael Schoeffler, Bernd Edler and JürgenHerre International Audio Laboratories Erlangen [email protected] Abstract. Researchers, developers and manufacturers put a lot of effort into improving the audio quality of their products but little is known about the overall resulting effect on the individual customer. An experiment with 34 participants was conducted with the purpose to investigate the relationship between audio quality and overall listening experience in more detail. When rating the overall listening experience, participants are allowed to take every aspect into account which is important to them, including their taste in music. The participants rated the overall listening experience of short music excerpts in different levels of bandwidth degradation. The result analysis revealed that listening to a song which is liked was slightly more important than the provided audio quality when participants rated the music excerpts. Nevertheless, audio quality was an important factor for less-liked songs to get higher ratings than more-liked songs with low or medium audio quality. Keywords: Overall Listening Experience, Quality of Experience, Audio Quality 1 Introduction The purpose of the experiment presented in this paper is to find out how important audio quality is for enjoying music. The term \overall listening experience" is used for describing the listener's enjoyment when listening to a music excerpt. When the overall listening experience is rated, listeners can take every aspect into account which seems important to them. Which aspects are important might differ from listener to listener but possible aspects could be mood, preferences in music genre, performing artist, expectations, lyrics and audio quality. The term overall listening experience is related to Quality of Experience (QoE). One can find slightly different definitions for Quality of Experience. Nevertheless, various authors share the opinion that it is an experience from the user's point of view. In Le Callet et al. [1] Quality of Experience is described as: \Quality of Experience (QoE) is the degree of delight or annoyance of the user of an application or service. It results from the fulfillment of his or her expectations with respect to the utility and/or enjoyment of the application or service in the light of the users personality and current state." CMMR2013 - 678 Proc. of the 10th International Symposium on Computer Music Multidisciplinary Research, Marseille, France, October 15-18, 2013 2 Michael Schoeffler et al. E. g. developers of audio products have an interest in answering the question how overall listening experience (or Quality of Experience) is related to audio quality. Although a lot of effort is put into improving the audio quality of products, but little is known about the effect on the individual customer. Therefore, the influence of audio quality on overall listening experience is investigated in this paper. There exist many possibilities to degrade audio quality, e. g. distortion in time or frequency domain, channel downmixing or bandwidth limitation. In this work the limitation of bandwidth is used for representing a degradation in audio quality. In methodologies for subjective evaluations of audio quality, like MUSHRA [2] or BS.1116-1 [3], listeners rate test items according to a given reference. In audio quality evaluation such a reference is the item having the optimal quality. In context of overall listening experience the definition of the optimal experience is listener-dependent. Every listener has a different understanding of the best overall listening experience and it is dependent on the listener's personal expectations, experiences etc. Such user-dependent variables are included in various Quality of Experience models [4] [5] [6]. When evaluating Quality of Experience by an experimental approach, it must be carefully dealt with the listeners's personal reference of optimal overall listening experience. In psychological research humans auditory memory is distinct in different types of storages depending on the decay times. Cowan separated them into short auditory storage and long auditory storage [7]. The short auditory storage refers to a literal storage that decays within a fraction of a second following a stimulus. On the other hand, the long auditory storage lasts for several seconds. While attending an experiment and listening to several stimuli in sequence, especially the short-term-related storages are probably altering by listening to each stimulus. Assuming the listener's definition of overall listening experience is somehow dependent on his or her auditory memories, the stimuli sequence might have an effect on the out- come of the experiment. Since the influence of auditory memories on rating the overall listening experience is not fully known, the experiment was designed to affect listeners' expectations and experiences (which are stored in the auditory memory) as little as possible. 2 Related Work Especially in telecommunications, Quality of Experience is becoming a popular topic. An introduction into Quality of Experience and an overview of the current state of the art of Quality of Experience research is given by Schatz et al. [8]. An overview of telecommunications-related QoE models including a proposed model was published by Laghari et al. [6]. Möller et al. developed a taxonomy of the most relevant Quality of Service and Quality of Experience aspects which result from multimodal human-machine interactions [9]. In context of Auditory Virtual Environments, Silzle developed a taxonomy based on point of views of end-users and service providers [10]. CMMR2013 - 679 Proc. of the 10th International Symposium on Computer Music Multidisciplinary Research, Marseille, France, October 15-18, 2013 Influence of Audio Quality on Overall Listening Experience 3 From a more acoustical point of view, Blauert and Jekosch proposed a four- layer model for the field of sound quality evaluation [11]. The most abstract layer of their proposed model is the Aural-communication Quality including the so-called product-sound quality which is the quality from an user's point of view. Schoeffler and Herre conducted an experiment to investigate the influence of audio quality on the overall listening experience [12]. The results of their experiment showed that overall listening experience is strongly influenced by bandwidth when the listeners is presented a reference of the music in maximum available bandwidth. However, the experiment took not into account the theory of auditory storages. Quality of Experience was rated in one session which famil- iarized the participants with audio quality and might have led to a bias towards increased audio-quality awareness. In evaluation of audio quality, listeners are often divided into expert listeners and na¨ıve listeners. Differences between these two types of listeners were investigated in recent publications [13] [14] [15] [16] [17]. 3 Method 3.1 Stimuli Selection Nine music excerpts were selected from a music database and were used as basic items. The decision for selecting these nine basic items was mainly based on their spectral centroids and their levels of awareness. The spectral centroid indicates where the \center of mass" of the spectrum is located and is defined as: PN−1 f(n) X(n) spectral centroid = n=0 ; (1) PN−1 n=0 X(n) where X(n) is the magnitude of bin number n, f(n) is the center frequency of bin number n and N is the total number of bins. Since the basic items were limited in their signal bandwidth in a subsequent pro- cessing step, the spectral centroid was used as a feature for roughly representing the perceived effect of limiting the signal bandwidth. E. g. when comparing a music excerpt with a very high spectral centroid and a music excerpt with a very low spectral centroid, limiting the bandwidth would more affect the music excerpt with the higher spectral centroid. The reason for this is that this music excerpt has more signal energy in the higher frequencies. Limiting the bandwidth would reduce the signal energy in the higher frequencies. This be- comes true, especially when the cut-off frequency for limiting the bandwidth is below the spectral centroid of the music excerpt with more signal energy in the higher frequencies. If the cut-off frequency is much higher than the spectral centroid of the music excerpt with less signal energy in the higher frequencies, the music excerpt might even not be affected. To overcome such situations a cluster of 20 music excerpts was computed from the music database. The cluster contained only music excerpts which have similar spectral centroids. The music CMMR2013 - 680 Proc. of the 10th International Symposium on Computer Music Multidisciplinary Research, Marseille, France, October 15-18, 2013 4 Michael Schoeffler et al. database consisted mainly of songs which were ranked in known record charts. Being ranked in a record chart was expected to represent the level of awareness. The music excerpts covered a recognizable part of the song (e. g. refrain) and had a duration of ten seconds. In a second step it was investigated which of these 20 music excerpts would lead to well distributed ratings according to listeners' music preference. There- fore, an informal pilot test with four participants was conducted. The participants rated the excerpts by a five-star scale according to how much they like each music excerpts. Based on the results of this informal pilot test, a group of three audio professionals decided the final selection of nine basic items. The selected nine basic items including their spectral centroids are depicted in Table 1. Song Interpret Centroid[Hz] American Pie Madonna 3620 Hold On Korn 3421 Hold on me Marlon Roudette 3636 Hotel California Eagles 3514 Love Today Mika 3887 No Light, No Light Florence and the Machine 3863 Nobody Knows Pink 3822 Respect Yourself Joe Cocker 3595 The Real Slim Shady Eminem 3819 Table 1: Selected music excerpts and their spectral centroid.

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