Replacing the Orchestra? – The Discernibility of Sample Library and Live Orchestra Sounds

Abstract

Recently, musical sounds from pre-recorded orchestra sample libraries (OSL) have become indispensable in music production for the stage or popular charts. Surprisingly, it is unknown whether human listeners can identify sounds as stemming from real orchestras or OSLs. Thus, an internet-based experiment was conducted to investigate whether a classic orchestral work, produced with sounds from a state-of-the-art OSL, could be reliably discerned from a live orchestra recording of the piece. It could be shown that the entire sample of listeners (N = 602) on average identified the correct sound source at 72.5%. This rate slightly exceeded Alan Turing's well-known upper threshold of 70% for a convincing, simulated performance. However, while sound experts tended to correctly identify the sound source, participants with lower listening expertise, who resembled the majority of music consumers, only achieved 68.6%. As non-expert listeners in the experiment were virtually unable to tell the real-life and OSL sounds apart, it is assumed that OSLs will become more common in music production for economic reasons.

Confusingly Similar: Discerning between Hardware Guitar Amplifier Sounds and Simulations with the Kemper Profiling Amp

“…All in all, this research is in line with previous research comparing new digital technology with analog sounds (Kopiez et al, 2016) and with research showing that neither listeners nor expert violinists could reliably distinguish old Italian violins (e.g., Stradivarius) from new violins (Fritz et al, 2017; Levitin, 2014). Neither study supports a general skeptical view that critics sometimes have of new music technology.…”

“…This field of research has an ongoing history and has examined, for example, whether listeners can distinguish a Stradivarius from a new violin (Fritz et al, 2017; Levitin, 2014) or a recording from a live band (Sharples, 2017). The previously outlined study by Kopiez et al (2016) also falls into this field of research.…”

Usability of Individualized Head-Related Transfer Functions in Virtual Reality: Empirical Study With Perceptual Attributes in Sagittal Plane Sound Localization

“…The “low expertise” group included, for instance, lay listeners, who might have been music lovers but were not trained musicians. The “high expertise” group included experienced listeners such as trained musicians, “Tonmeister,” and sound engineers [ 41 ]. To determine the required number of subjects, we conducted an a priori power analysis with the software program G*Power (Heinrich Heine University Düsseldorf).…”

The role of divided attention and expertise in melody recognition

“…The statistical approach used linear mixed-effects models in order to adequately capture random stimuli and participant effects (Baayen, Davidson, & Bates, 2008). The models predicted recognition performance in the form of participant-wise d ’ values: Z(Hit rate) − Z(False alarm rate), where the hit rate is the percentage of correct responses on each of the three melody repetitions, and the false alarm rate is the percentage of incorrect responses on the first presentation of each melody (Kopiez, Wolf, Platz, & Mons, 2016; Macmillan & Creel, 2005). We use a log linear approach to correct extreme values (Hautus, 1995).…”