LISEL

LISEL - PATTERNS FOR AUTO-TUNED VOICES AND DELAY IS OUT NOW

Eliza Bagg leads a complex musical life: working as a classical opera singer, she has soloed with the New York Philharmonic, performed in Meredith Monk’s opera at the Los Angeles Philharmonic, and toured Europe with the legendary John Zorn. While making her own music under the guise of art-pop solo act Lisel, she’s also collaborated as a vocalist with some of the most renowned experimental artists, including Ben Frost, esperanza spalding, Nico Muhly, Julianna Barwick, David Lang, Lyra Pramuk, Daniel Wohl, and Bryce Dessner, all while playing indie rock venues and lovably dingy basements. One day, it’s Lincoln Center or The Kitchen, the next it’s an outdoor LA ambient series. She was always torn between her two worlds, and it wasn’t until she began work on Patterns for Auto-tuned Voices and Delay that she discovered a way to merge them together.

Patterns comes out of Bagg’s experience as a vocalist singing Renaissance and Baroque music along with the work of modern-day minimalists like Steve Reich and Philip Glass. “I developed a vocal processing system that allowed me to change the idea of what my instrument is,” Bagg says of the album's genesis, a system that combines her virtuosic singing with autotune and delay effects to create a melding of human and machine. After years of using her voice in highly specialized ways (as in singing the music of Caroline Shaw with Roomful of Teeth), Bagg wanted to explore what that level of vocal technicality can do when combined with technology. What results is a full spectrum sound journey through the potential of the human voice; the new Lisel album explores a world of singing that maintains melody as it pushes boundaries. “I rely on my body as an object and resonant instrument,” she says. “Now, what begins inside my body and continues on the computer is one process, and the ideas that result from it are my instrument.”

Sure, she’ll admit it. “I’m a sci-fi nerd,” she says, with a laugh. "I’m a Blade Runner and Battlestar person. I love things that explore how society interacts with machines.” Recently, her thoughts have focused on the hovering feeling of apocalypse along with the question of our own obsolescence. It was only a matter of time before she absorbed the capabilities of technology into her own music. Through experiments with Ableton, she realized she was making music that was more expressive of humanity, not less. “We are tempted to see rapid technological change as an impediment to the traditions that ground us. But I feel our connection to ancient forms can be amplified and transformed by this new reality, not lost.” Bagg says.

While making Patterns, she dove first into Renaissance polyphony and chant. The music of Hildegard von Bingen, Thomas Tallis, and Carlo Gesualdo is a familiar world to her. Starting with Renaissance and Medieval singing styles and idioms, she added processing and electronic world-building to bring out new, expressive qualities of those styles. From there, she improvised in these styles, fed the performances into Ableton, and incorporated modern day hyperpop (like SOPHIE) and ambient electric sounds and aesthetics - as she says, “I am going for a maximalist sound, but my sources of inspiration also include minimalists.” From Philip Glass to Charli XCX, Carl Stone to Grimes, Patterns makes radical connections.

Yet, it was important for Bagg to maintain the spiritual origins of these vocal techniques. As she describes it, “The album uses layered singing as a pathway to spirituality, as it has been throughout history.” Patterns for Auto-tuned Voices and Delay stands within those traditions, using voices to transcend the cerebral and overwhelm the listener, all while evoking a unique set of references that span 500 years.

As a vocalist, Bagg has collaborated and performed with Meredith Monk, Ben Frost, Esperanza Spalding, Nico Muhly, Julianna Barwick, David Lang, Lyra Pramuk, Daniel Wohl, and Caroline Shaw

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Lisel, Jeremiah Chiu & Marta Sofia Honer, Medium Soda

2220 Arts + Archives

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